Technology Acceptance Model 3 and a Research Agenda on Interventions
Open accessAbstract
ABSTRACT Prior research has provided valuable insights into how and why employees make a decision about the adoption and use of information technologies (ITs) in the workplace. From an organizational point of view, however, the more important issue is how managers make informed decisions about interventions that can lead to greater acceptance and effective utilization of IT. There is limited research in the IT implementation literature that deals with the role of interventions to aid such managerial decision making. Particularly, there is a need to understand how various interventions can influence the known determinants of IT adoption and use. To address this gap in the literature, we draw from the vast body of research on the technology acceptance model (TAM), particularly the work on the determinants of perceived usefulness and perceived ease of use, and: (i) develop a comprehensive nomological network (integrated model) of the determinants of individual level (IT) adoption and use; (ii) empirically test the proposed integrated model; and (iii) present a research agenda focused on potential pre‐ and postimplementation interventions that can enhance employees' adoption and use of IT. Our findings and research agenda have important implications for managerial decision making on IT implementation in organizations.
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Claims the paper makes
2 of 55 cited sources checked · 1 supported · 0 flagged · accuracy not yet measured
- Premisecitation
Mood during training plays an important role in forming individuals' perceptions of a new IT.
“Venkatesh and Speier (1999) investigated the effect of mood during training on user acceptance and found that mood during training played an important role in forming individuals' perceptions of a new IT.”
Quote in the paperNot a substantial claim1 of 1 sources checked- cites Venkatesh & Speier, 1999 source
- Findingown data
The two-way interaction of social norm and voluntariness showed that social norm's effect on intention was stronger when use was mandatory.
“The two-way interaction between subjective norm and voluntariness (SN × EXP) indicated that the effect of subjective norm on behavioral intention was stronger in a mandatory context.”
Quote in the paperSubstantial claim- Rests on an assumption: Participants who completed all measurement points are representative of the initial user populations at the four sites.
- Rests on an assumption: The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
- Findingown data
Experience weakened the effect of computer anxiety on ease of use as experience increased.
“we found that experience moderated the effect of computer anxiety on perceived ease of use such that the effect became weaker with increasing experience (CANX × EXP).”
Quote in the paperSubstantial claim- Rests on an assumption: Participants who completed all measurement points are representative of the initial user populations at the four sites.
- Rests on an assumption: The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
- Findingown data
Experience strengthened the effect of ease of use on usefulness as experience increased.
“experience moderated the effect of perceived ease of use on perceived usefulness such that with increasing experience the effect became stronger.”
Quote in the paperSubstantial claim- Rests on an assumption: Participants who completed all measurement points are representative of the initial user populations at the four sites.
- Rests on an assumption: The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
- Findingown data
The model accounted for 52% to 67% of usefulness variance across time periods and models.
“Overall, TAM3 was able to explain between 52% and 67% of the variance in perceived usefulness across different time periods and models (see Table 5).”
Quote in the paperSubstantial claim- Rests on an assumption: Participants who completed all measurement points are representative of the initial user populations at the four sites.
- Rests on an assumption: The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
- Findingown data
Job relevance and output quality interacted in predicting usefulness: as output quality rose, job relevance's effect on usefulness became stronger.
“we found that job relevance and output quality had an interactive effect on perceived usefulness such that with increasing output quality, the effect of job relevance on perceived usefulness was stronger.”
Quote in the paperSubstantial claim- Rests on an assumption: Participants who completed all measurement points are representative of the initial user populations at the four sites.
- Rests on an assumption: The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
- Findingown data
Experience weakened the effect of ease of use on intention as experience increased.
“We found that experience, in fact, moderated the effect of perceived ease of use (PEOU × EXP) on behavioral intention such that with increasing experience the effect became weaker.”
Quote in the paperSubstantial claim- Rests on an assumption: Participants who completed all measurement points are representative of the initial user populations at the four sites.
- Rests on an assumption: The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
- Findingown data
The square root of AVE for each construct exceeded its correlations with other constructs.
“The square root of the average variance extracted (AVE) for each construct was higher than the correlations across constructs.”
Quote in the paperSubstantial claim - Findingown data
Internal consistency reliabilities exceeded .70 for all constructs at all measurement points.
“As Table 4 shows, internal consistency reliabilities (ICRs) were greater than .70 for all constructs at all points of measurement.”
Quote in the paperSubstantial claim - Findingown data
Item loadings were at least .70 for every construct at every time period.
“Table 3 shows that the item loadings were greater than or at least equal to .70 for all constructs at all time periods.”
Quote in the paperSubstantial claim - Findingown data
Image significantly affected social norm at every measurement point.
“we found that the effect of image on subjective norm was significant at all points of measurements.”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants who completed all measurement points are representative of the initial user populations at the four sites.
- Rests on an assumption: The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
- Findingown data
No cross-loading exceeded .30.
“We did not find any cross-loadings of more than .30.”
Quote in the paperSubstantial claim - Findingown data
Usefulness was the strongest predictor of intention at every time point.
“We found that perceived usefulness was the strongest predictor of behavioral intention at all time periods (see Table 7).”
Quote in the paperSubstantial claim- Rests on an assumption: Participants who completed all measurement points are representative of the initial user populations at the four sites.
- Rests on an assumption: The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
- Findingown data
Use variance explained ranged from 31% to 36%.
“Table 8 shows that the variance explained in use was between 31% and 36%.”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants who completed all measurement points are representative of the initial user populations at the four sites.
- Rests on an assumption: The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
- Findingown data
The anchors-computer self-efficacy, external control perceptions, computer anxiety, and computer playfulness-significantly predicted ease of use at every measurement point.
“we found that the anchors-that is, computer self-efficacy, perceptions of external control, computer anxiety, and computer playfulness-were significant predictors of perceived ease of use at all points of measurement (see Table 6).”
Quote in the paperSubstantial claim- Rests on an assumption: Participants who completed all measurement points are representative of the initial user populations at the four sites.
- Rests on an assumption: The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
- Findingown data
No usefulness determinant significantly affected ease of use.
“Our results indicated that none of the determinants of perceived usefulness had a significant effect on perceived ease of use.”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants who completed all measurement points are representative of the initial user populations at the four sites.
- Rests on an assumption: The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
- Findingown data
No ease-of-use determinant significantly affected usefulness at any time point.
“none of the determinants of perceived ease of use had significant effects on perceived usefulness at any point in time.”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants who completed all measurement points are representative of the initial user populations at the four sites.
- Rests on an assumption: The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
- Findingown data
The model accounted for 40% to 53% of intention variance across time periods and models.
“Table 7 shows that TAM3 explained between 40% and 53% variance in behavioral intention across different time periods and models.”
Quote in the paperSubstantial claim- Rests on an assumption: Participants who completed all measurement points are representative of the initial user populations at the four sites.
- Rests on an assumption: The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
- Findingown data
The model accounted for 43% to 52% of ease-of-use variance across time points and models.
“Overall, TAM3 explained between 43% and 52% of variance in perceived ease of use across different points of measurements and models (see Table 6).”
Quote in the paperNot a substantial claim- Rests on an assumption: Participants who completed all measurement points are representative of the initial user populations at the four sites.
- Rests on an assumption: The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
- Findingown data
Experience weakened the effect of social norm on usefulness as experience increased.
“We found that experience moderated the effects of subjective norm on perceived usefulness such that the effect was weaker with increasing experience.”
Quote in the paperSubstantial claim- Rests on an assumption: Participants who completed all measurement points are representative of the initial user populations at the four sites.
- Rests on an assumption: The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
- Findingown data
Intention significantly predicted use at every measurement point.
“we found that behavioral intention was a significant predictor of use at all points of measurements.”
Quote in the paperSubstantial claim- Rests on an assumption: Participants who completed all measurement points are representative of the initial user populations at the four sites.
- Rests on an assumption: The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
- Findingown data
A three-way interaction of social norm, experience, and voluntariness affected intention: social norm's effect on intention weakened with experience, especially in voluntary settings.
“We also found a significant three-way interaction among subjective norm, experience, and voluntariness (SN × EXP × VOL) on behavioral intention such that the effect of subjective norm on behavioral intention became weaker with increasing experience, particularly in the voluntary context.”
Quote in the paperSubstantial claim- Rests on an assumption: Participants who completed all measurement points are representative of the initial user populations at the four sites.
- Rests on an assumption: The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
- Findingown data
Behavioral intention has a positive effect on use at T2, T3, T4, and pooled (β = .57, .56, .60, .59, p < .001).
“Behavioral | .57 * * * | .56 * * * | .60 * * * | .59 * * *”
Quote in the paperSubstantial claim - Findingown data
Perceptions of external control have a positive effect on perceived ease of use at T1, T2, T3, and pooled (β = .37, .30, .30, .33, p < .001).
“Perceptions of Ext. Control (PEC) | .37 * * * | .30 * * * | .30 * * * | .33 * * *”
Quote in the paperSubstantial claim - Findingown data
Computer playfulness has a positive effect on perceived ease of use at T1, T2, T3, and pooled (β = .20, .16, .11, .15, p < .05 or better).
“Computer Playfulness (CPLAY) | .20 * * | .16 * | .11 * | .15 * *”
Quote in the paperSubstantial claim - Findingown data
In the pooled sample, the interaction between subjective norm and experience has a negative effect on perceived usefulness (β = -.29, p < .001).
“SN × EXP | | | | -.29 * * *”
Quote in the paperSubstantial claim - Findingown data
The pooled model explains 67% of the variance in perceived usefulness.
“R 2 | .60 | .56 | .52 | .67”
Quote in the paperSubstantial claim - Findingreasoning
The paper provided theoretical justification and empirical support that determinants of perceived ease of use, such as computer self-efficacy, will not have significant effects on perceived usefulness over and above known determinants driven by social influence and cognitive instrumental processes.
“We provided the theoretical justification and empirical support of why the determinants of perceived ease of use (e.g., computer self-efficacy) will not have significant effects on perceived usefulness over and above the known determinants of perceived usefulness that are driven by the social influence and cognitive instrumental processes.”
Quote in the paperSubstantial claim - Findingown data
The pooled model explains 53% of the variance in behavioral intention.
“R 2 | .48 | .44 | .40 | .53”
Quote in the paperSubstantial claim - Findingown data
In the pooled sample, the interaction between perceived ease of use and experience has a negative effect on behavioral intention (β = -.24, p < .001).
“PEOU × EXP | | | | -.24 * * *”
Quote in the paperSubstantial claim - Findingown data
The pooled model explains 52% of the variance in perceived ease of use.
“R 2 | .43 | .45 | .44 | .52”
Quote in the paperNot a substantial claim - Findingown data
In the pooled sample, the interaction between perceived ease of use and experience has a positive effect on perceived usefulness (β = .39, p < .001).
“EOU × EXP | | | | .39 * * *”
Quote in the paperSubstantial claim - Findingown data
Computer anxiety has a negative effect on perceived ease of use at T1, T2, T3, and pooled (β = -.22, -.18, -.14, -.18, p < .05 or better).
“Computer Anxiety (CANX) | -.22 * * * | -.18 * * | -.14 * | -.18 * *”
Quote in the paperSubstantial claim - Findingown data
Computer self-efficacy has a positive effect on perceived ease of use at T1, T2, T3, and pooled (β = .35, .30, .28, .31, p < .001).
“Computer Self-Efficacy (CSE) | .35 * * * | .30 * * * | .28 * * * | .31 * * *”
Quote in the paperSubstantial claim - Findingown data
The interaction between job relevance and output quality has a positive effect on perceived usefulness at T1, T2, T3, and pooled (β = .37, .34, .35, .35, p < .001).
“REL × OUT | .37 * * * | .34 * * * | .35 * * * | .35 * * *”
Quote in the paperSubstantial claim - Findingown data
Image has a positive effect on perceived usefulness at T1, T2, T3, and pooled (β = .27, .20, .23, .24, p < .05 or better).
“Image (IMG) | .27 * * * | .20 * * | .23 * * * | .24 * * *”
Quote in the paperSubstantial claim - Findingown data
In the pooled sample, the three-way interaction between subjective norm, experience, and voluntariness has a negative effect on behavioral intention (β = -.46, p < .001).
“SN × EXP × VOL | | | | -.46 * * *”
Quote in the paperSubstantial claim - Findingown data
Perceived usefulness has a positive effect on behavioral intention at T1, T2, T3, and pooled (β = .55, .56, .57, .56, p < .001).
“Perceived Usefulness (PU) | .55 * * * | .56 * * * | .57 * * * | .56 * * *”
Quote in the paperSubstantial claim - Findingown data
The pooled model explains 35% of the variance in use.
“R 2 | .32 | .31 | .36 | .35”
Quote in the paperNot a substantial claim - Interpretationreasoning
Having control over a system does not ensure that it will improve one's job performance.
“For example, control over using a system does not guarantee that the system will enhance one's job performance.”
Quote in the paperSubstantial claim - Premisecitation
TAM2 received strong support from Venkatesh and Davis's longitudinal field studies at four organizations.
“Venkatesh and Davis found strong support for TAM2 in longitudinal field studies conducted at four organizations.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Venkatesh & Davis, 2000 source
- Premisecitation
TAM explains roughly 40% of the variability in people's intention to use an IT and their actual usage.
“TAM consistently explains about 40% of the variance in individuals' intention to use an IT and actual usage.”
Quote in the paperNot a substantial claim0 of 2 sources checked - Premisecitation
Venkatesh (2000) proposed determinants of perceived ease of use that are mainly individual difference variables and general beliefs about computers and their use.
“The determinants of perceived ease of use suggested by Venkatesh (2000) are primarily individual differences variables and general beliefs about computers and computer use.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Venkatesh, 2000
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationcitation
Greater computer playfulness or enjoyment from system use does not imply that the system will make a person more effective.
“Similarly, higher levels of computer playfulness or enjoyment from using a system do not mean that the system will help an individual to become more effective (e.g., Van der Heijden, 2004).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Van der Heijden, 2004 source
- Study design and conductown data
The study collected longitudinal field data from four organizations implementing new information technologies.
“Longitudinal field studies were conducted to test TAM3.Data were collected from four different organizations-sites A through D-implementing new ITs.”
Quote in the paperSubstantial claim - Premisecitation
When employees begin using a new system, they are more likely to experience substantial changes to intrinsic job characteristics, work processes, routines, and habits.
“When employees start using a new system, as noted earlier, they are more likely to experience substantial changes to their intrinsic job characteristics, work processes, routines, and habits (Millman & Hartwick, 1987).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Millman & Hartwick, 1987 source
- Premisecitation
In decision support systems and group support systems use contexts, incentive has been found to be an important factor.
“However, in decision support systems and group support systems use contexts (e.g., Mennecke & Valacich, 1998;Speier et al., 2003), incentive has been found to be an important factor (see Todd & Benbasat, 1999).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Todd & Benbasat, 1999 source
- Study design and conductreasoning
The longitudinal design provided a procedural remedy for common method bias.
“While the longitudinal design provided us a procedural remedy for common method bias”
Quote in the paperNot a substantial claim - Interpretationreasoning
Postimplementation interventions can be crucial to help employees through the initial shock and changes associated with a new system.
“While preimplementation interventions are designed and implemented in order to reduce initial resistance and develop realistic perceptions of system features, capabilities, and relevance, postimplementation interventions can be crucial to help employees go through the initial shock and changes associated with the new system.”
Quote in the paperSubstantial claim - Premisecitation
TAM3 treats perceived voluntariness, not actual voluntariness, as an important contextual variable.
“consistent with TAM2, TAM3 posits perceived, rather than actual, voluntariness as an important contextual variable.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites TAM2
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
Incentive alignment can influence employees' perceptions of job relevance, output quality, and results demonstrability.
“Incentive alignment can potentially influence employees' perceptions of job relevance, output quality, and results demonstrability of a system.”
Quote in the paperSubstantial claim - Interpretationnone given
There are no theoretical or empirical grounds to expect that stable computer-related traits and emotions are influenced by social or cognitive influence processes.
“There are no theoretical and empirical reasons to believe that these stable computer-related traits and emotions will be affected by social influence or cognitive influence processes.”
Quote in the paperNot a substantial claim - Premisecitation
The IT implementation literature contains little research on interventions that help managers make decisions about IT acceptance and use.
“There is limited research in the IT implementation literature that deals with the role of interventions to aid such managerial decision making.”
- Also stated in: § † † Effective July 1, 2008. · plain
“there has been limited research on the interventions that can potentially lead to greater acceptance and use of IT (Venkatesh, 1999).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Venkatesh, 1999
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Also stated in: § † † Effective July 1, 2008. · plain
- Interpretationcitation
A high-quality system that users view positively may still fail to produce positive organizational outcomes if users have no incentive to use it effectively.
“For example, even if a system is of high quality, from a system engineering point of view and users may develop positive attitudes toward the system from a technology acceptance point of view, it may not lead to positive organizational outcomes if there are no incentives in place for the users for using the system effectively.”
Quote in the paperSubstantial claim - Interpretationreasoning
Incentive alignment can be an important preimplementation intervention that potentially improves user acceptance.
“We suggest that incentive alignment can be an important intervention in the preimplementation stage that can potentially enhance user acceptance.”
Quote in the paperNot a substantial claim- Rests on an assumption: There is limited research on the role of incentive alignment in IT adoption contexts.
- Premisecitation
Three relationships not previously tested by Venkatesh (2000) or Venkatesh and Davis (2000) are proposed in TAM3.
“TAM3 posits three relationships that were not empirically tested in Venkatesh (2000) and Venkatesh and Davis (2000).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Venkatesh, 2000
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Venkatesh & Davis, 2000 source
- Interpretationreasoning
Incentives can influence subjective norm and image when system use is noticed and rewarded by management.
“Given that their use of the system will be noticed and rewarded by the management, incentives can influence subjective norm and image.”
Quote in the paperNot a substantial claim - Premisecitation
In IT adoption, social influence processes reflect what important referents think about a system's instrumental benefits.
“Social influence processes (i.e., compliance, identification, and internalization) in the context of IT adoption and use represent how important referents believe about the instrumental benefits of using a system (Venkatesh & Davis, 2000).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Venkatesh & Davis, 2000 source
- Premisecitation
Theory links perceived ease of use closely to self-efficacy beliefs and procedural knowledge, which require hands-on experience and skill execution.
“Perceived ease of use has been theorized to be closely associated with individuals' self-efficacy beliefs and procedural knowledge, which requires handson experience and execution of skills (Davis et al., 1989;Venkatesh, 2000;Davis & Venkatesh, 2004).”
Quote in the paperSubstantial claim0 of 3 sources checked - Premisecitation
Changing perceptions can play an important role in determining individuals' continuance intention and long-term use of a system.
“The changing perceptions can play an important role in determining individuals' continuance intention and long-term use of a system (Bhattacherjee, 2001).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Bhattacherjee, 2001 source
- Premisecitation
Game-based training is more effective than traditional training for enhancing user acceptance of a new system.
“For example, Venkatesh (1999) found that game-based training was more effective than traditional training to enhance user acceptance of a new system.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Venkatesh, 1999
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Low adoption and high underutilization of ITs have been a major problem for organizations in realizing benefits of IT implementations.
“Further, low adoption and high underutilization of ITs have been a major problem for organizations in terms of realizing the benefits (both tangible and intangible) of IT implementations (Jasperson et al., 2005)”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Jasperson et al., 2005
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
Individuals' reactions toward an IT may change over time.
“Experience is an important moderating variable in IT adoption contexts because, as suggested in much prior research, individuals' reactions toward an IT may change over time (Karahanna et al., 1999;Bhattacherjee & Premkumar, 2004).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Karahanna et al., 1999
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- cites Bhattacherjee & Premkumar, 2004 source
- Premisecitation
During postimplementation, employees' coping strategies depend on whether they perceive the system as a threat or opportunity and whether they have control over it.
“During postimplementation stages, employees attempt to cope with the new system in different ways depending on whether they perceive the system as a threat (or an opportunity) and whether they have control over the system (Beaudry & Pinsonneault, 2005).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Beaudry & Pinsonneault, 2005 source
- Premisecitation
Due to the magnitude of these impacts, employees are reluctant to adopt new ITs.
“Due to the magnitude of these impacts, employees are reluctant to adopt new ITs (Lapointe & Rivard, 2005).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Lapointe & Rivard, 2005 source
- Interpretationcitation
Peer support may influence subjective norm and image associated with using a system.
“Finally, peer support may also influence subjective norm and image associated with using a system.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Employees' job outcomes, such as job satisfaction and job performance, can be affected by complex IT implementation.
“Consequently, employees' job outcomes (e.g., job satisfaction and job performance) can be affected.”
Quote in the paperSubstantial claim - Premisecitation
In the postimplementation stage, the presence of different types of support is very important, particularly for complex systems that are inherently difficult to understand and use.
“In the postimplementation stage, the presence of different types of support is very important, particularly in the context of complex systems, (e.g., enterprise systems) that are inherently difficult to understand and use (e.g., Bajwa, Rai, & Brennan, 1998).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Bajwa, Rai, & Brennan, 1998 source
- Premisecitation
Interventions such as user participation and training can help employees decide how to cope with or adapt a new IT.
“Some other interventions (e.g., user participation, training) can help employees decide how to cope with or adapt a new IT (Beaudry & Pinsonneault, 2005).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Beaudry & Pinsonneault, 2005 source
- Premisecitation
If coworkers are favorable toward a new system, employees are more likely to form favorable perceptions through social influence processes.
“If coworkers are favorable toward a new system, it is more likely that employees will form favorable perceptions toward the system through social influence processes (Venkatesh & Davis, 2000).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Venkatesh & Davis, 2000 source
- Premisecitation
Long-term use of a system is a key measure of ultimate success.
“While initial adoption is important, long-term use of a system is a key measure of ultimate success of a system (Rai et al., 2002;DeLone & McLean, 2003).”
Quote in the paperSubstantial claim0 of 2 sources checked- cites Rai et al., 2002 source
- cites DeLone & McLean, 2003
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Interpretationreasoning
Peer support through formal and informal training can enhance users' understanding of a system.
“First, peer support through formal and informal training can enhance users' understanding of a system.”
Quote in the paperSubstantial claim- Rests on an assumption: There is little or no research on the role of peer support in the context of IT adoption.
- Interpretationreasoning
Perceived ease of use remains an important user reaction toward IT even when users have substantial hands-on experience.
“This clearly indicates that perceived ease of use is still an important user reaction toward IT even if users have substantial hands-on experience with the IT.”
Quote in the paperSubstantial claim - Interpretationreasoning
The presence of organizational support, particularly for complex systems, can reduce anxiety associated with system use.
“Further, the presence of organizational support, particularly in the context of complex systems, can reduce anxiety associated with system use.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Organizational support can play a key role in determining perceived usefulness and perceived ease of use.
“Thus, organizational support can play a key role in determining perceived usefulness and perceived ease of use.”
Quote in the paperSubstantial claim - Premisecitation
Internal or external experts are important sources of interventions.
“Jasperson et al. (2005) noted the importance of internal or external experts as sources of interventions.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Jasperson et al., 2005
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Premisecitation
If employees perceive a new system as a threat and lack necessary resources and abilities, they are more likely to avoid it.
“For example, if employees perceive that a new system is a threat to their well-being and they do not have necessary resources and abilities to use the system, it is more likely that they will try to avoid the new system (Beaudry & Pinsonneault, 2005).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Beaudry & Pinsonneault, 2005 source
- Interpretationreasoning
Design characteristics such as extent of customization or complexity can reduce changes in employees' jobs by enhancing fit between system and jobs.
“For example, in the context of enterprise systems, certain design characteristics (e.g., extent of customization or complexity of the system) can reduce changes in employees' jobs as these characteristics can potentially enhance the fit between a system and employees' jobs.”
Quote in the paperNot a substantial claim - Interpretationreasoning
Training can help users develop favorable perceptions of different determinants of perceived usefulness and perceived ease of use.
“These findings indicate that training can be used to help users develop favorable perceptions of different determinants of perceived usefulness and perceived ease of use.”
Quote in the paperSubstantial claim - Interpretationreasoning
Users may get insights from peers on job relevance, output quality, and result demonstrability of a system.
“Thus, users may get insights from their peers on job relevance, output quality, and result demonstrability of a system.”
Quote in the paperSubstantial claim - Premisecitation
Implementation of complex ITs and associated business process changes have a profound impact on employees' jobs, causing changes in job characteristics, workplace relationships, and other job aspects.
“As discussed earlier, implementation of complex ITs (e.g., enterprise systems, interorganizational systems) and associated changes in business processes have a profound impact on employees' job and cause changes in their job characteristics, relationships with others in the workplace, and other aspects of their job (Boudreau & Robey, 2005;Lapointe & Rivard, 2005).”
Quote in the paperSubstantial claim0 of 2 sources checked - Interpretationreasoning
Different modes of training can be used to manipulate different determinants of IT adoption.
“One of the key reasons for training to be an important intervention is that different modes of training can be used to manipulate different determinants of IT adoption.”
Quote in the paperSubstantial claim - Interpretationreasoning
TAM3 posits that experience moderates key relationships in IT adoption.
“TAM3 posits new theoretical relationships such as the moderating effects of experience on key relationships.”
Quote in the paperSubstantial claim - Premisecitation
Experts can help users modify or enhance IT applications or work processes.
“These experts can help users modify or enhance the IT applications or work processes (Jasperson et al., 2005).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Jasperson et al., 2005
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- May combine two claims. 25
- The quote only partly states this claim. 4
- Classified as probably not a substantial claim. 26
May combine two claims.
PremisecitationIncentive alignment, as an extrinsic reward, may reduce anxiety and increase perceived enjoyment because extrinsic rewards are considered important drivers of intrinsic motivations.
“Further, incentive alignment, and an important extrinsic reward, may reduce anxiety and increase perceived enjoyment as extrinsic rewards are considered important drivers of intrinsic motivations (Vallerand, 1997;Deci, Koestner, & Ryan, 1999;Ryan & Deci, 2000).”
Quote in the paperNot a substantial claim1 of 3 sources checked- cites Vallerand, 1997 source
- cites Deci, Koestner, & Ryan, 1999 source
The Results report that verbal rewards (positive feedback) enhanced self-reported interest, which offers limited support for the possibility that an extrinsic reward could increase enjoyment. But the paper does not assess incentive alignment or anxiety, and its Concluding Comments (pp. 658–659) say tangible rewards generally diminish rather than promote intrinsic motivation. The cited source therefore does not support the proposed effect of incentive alignment. (LLM, conf=0.83)
Experimental — accuracy not yet measured - cites Ryan & Deci, 2000 source
May combine two claims.
Findingown dataThe ease-of-use construct, social norm, image, and result demonstrability each significantly predicted usefulness at every time point.
“we found that perceived ease of use, subjective norm, image, and result demonstrability were significant predictors of perceived usefulness at all time periods (see Table 5).”
Quote in the paperSubstantial claim- Rests on an assumption: Participants who completed all measurement points are representative of the initial user populations at the four sites.
- Rests on an assumption: The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
May combine two claims.
Findingown dataEase of use significantly predicted intention at T1 and T2 but not T3, suggesting experience moderated that relationship.
“While perceived ease of use was significant at T1 and T2, it was not significant at T3, suggesting a moderating effect of experience in the relationship between perceived ease of use and behavioral intention.”
Quote in the paperSubstantial claim- Rests on an assumption: Participants who completed all measurement points are representative of the initial user populations at the four sites.
- Rests on an assumption: The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
May combine two claims.
Findingown dataThe adjustments-perceived enjoyment and objective usability-were nonsignificant at T1 but significant at T2 and T3.
“the adjustments-that is, perceived enjoyment and objective usability-were not significant at T1, but they were significant at both T2 and T3.”
Quote in the paperSubstantial claim- Rests on an assumption: Participants who completed all measurement points are representative of the initial user populations at the four sites.
- Rests on an assumption: The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
The quote only partly states this claim.
Findingown dataIn the pooled sample, the interaction between objective usability and experience has a positive effect on perceived ease of use (β = .20, p < .01).
“OU × EXP | | | | .20 * *”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataObjective usability has a positive effect on perceived ease of use at T2 and T3, but not at T1 or pooled.
“Objective Usability (OU) | .04 | .19 * * | .23 * * * | .03”
Quote in the paperSubstantial claimThe quote only partly states this claim.
Findingown dataIn the pooled sample, the interaction between computer anxiety and experience has a positive effect on perceived ease of use (β = .21, p < .001).
“CANX × EXP | | | | .21 * * *”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataSubjective norm has a positive effect on perceived usefulness at T1, T2, and T3, but not in the pooled sample.
“Subjective Norm (SN) | .40 * * * | .32 * * * | .13 * | .04”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataThe interaction between subjective norm and voluntariness has a positive effect on behavioral intention at T1, T2, and T3, but not in pooled.
“SN × VOL | .29 * * * | .22 * * * | .17 * | .03”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataPerceived enjoyment has a positive effect on perceived ease of use at T2 and T3, but not at T1 or pooled.
“Perceived Enjoyment (ENJ) | .02 | .22 * * * | .24 * * * | .04”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing), S4 (based).
Findingown dataResult demonstrability has a positive effect on perceived usefulness at T1, T2, T3, and pooled (β = .22, .26, .28, .26, p < .001).
“Result Demonstrability (RES) | .22 * * * | .26 * * * | .28 * * * | .26 * * *”
Quote in the paperNot a substantial claimThe quote only partly states this claim.
Findingown dataIn the pooled sample, the interaction between computer playfulness and experience has a negative effect on perceived ease of use (β = -.22, p < .001).
“CPLAY × EXP | | | | -.22 * * *”
Quote in the paperSubstantial claimMay combine two claims.
Findingown dataPerceived ease of use has a positive effect on perceived usefulness at T1, T2, and T3, but not in the pooled sample.
“Perceived Ease of Use (PEOU) | .22 * * * | .26 * * * | .33 * * * | .08”
Quote in the paperSubstantial claimThe quote only partly states this claim.
Findingown dataIn the pooled sample, the interaction between perceived enjoyment and experience has a positive effect on perceived ease of use (β = .18, p < .01).
“ENJ × EXP | | | | .18 * *”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataPerceived ease of use has a positive effect on behavioral intention at T1 and T2, but not at T3 or pooled.
“Perceived Ease of Use (PEOU) | .24 * * * | .17 * | .05 | .04”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationThe moderating role of experience was not tested in Venkatesh (2000), despite longitudinal studies being conducted.
“Although longitudinal studies were conducted, the specific moderating role by experience was not tested in Venkatesh (2000).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Venkatesh, 2000
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
InterpretationreasoningIn enterprise systems, if an individual perceives that their system use benefits other work units rather than their own, they will perceive a lack of incentive alignment, which may lead to low user acceptance and use.
“For example, in the context of enterprise systems, if an individual perceives that his or her use of the system does not benefit the members of his or her work units but rather benefits members from other work units, the user will perceive a lack of incentive alignment that may lead to low user acceptance and use of the system.”
Quote in the paperSubstantial claimMay combine two claims.
Study design and conductown dataHarmon's single-factor test and a marker-variable test using job satisfaction found no significant common method bias.
“we conducted Harmon's single factor test and marker variable test (we used job satisfaction as a marker variable) and did not find any significant common method bias.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductreasoningThe study used validated items from earlier research to test TAM3.
“We used validated items from prior research to test TAM3.”
Quote in the paperNot a substantial claimMay combine two claims.
Study design and conductreasoningThe design measured use at least one month apart from its determinants, which helped reduce common method bias.
“Our research design allowed us to collect the use data separate from its determinants (e.g., behavioral intention, perceived usefulness, etc.).Particularly, there was at least a 1-month gap between the collection of survey data and the measurement of use.”
- Also stated in: § Measurement · plain
“Such a design approach helped us overcome the problems associated with common method biases.”
Quote in the paperSubstantial claim- Also stated in: § Measurement · plain
Probably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitationThe determinants of ease of use were operationalized consistently with Venkatesh (2000).
“Operationalization of the determinants of perceived ease of use (i.e., computer self-efficacy, perceptions of external control, computer playfulness, computer anxiety, objective usability, and perceived enjoyment) was consistent with Venkatesh (2000).”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Venkatesh (2000)
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Classified as probably not a substantial claim.
PremisecitationTAM holds that perceived usefulness and perceived ease of use determine a person's behavioral intention to use an IT.
“It posits that individuals' behavioral intention to use an IT is determined by two beliefs: perceived usefulness, defined as the extent to which a person believes that using an IT will enhance his or her job performance and perceived ease of use, defined as the degree to which a person believes that using an IT will be free of effort.”
Quote in the paperSubstantial claim0 of 2 sources checkedClassified as probably not a substantial claim.
PremisecitationBa et al. argued that organizations may not obtain expected benefits from employees' effective system use unless employees perceive that system features and capabilities match their interests and incentives.
“Ba et al. (2001) argued that while aspects of software engineering (e.g., system characteristics) and technology acceptance (e.g., perceived usefulness, perceived ease of use, user satisfaction) are important considerations for system development processes, organizations may fail to gain expected benefits from employees' effective utilization of a system unless employees find that the system fe”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ba et al., 2001
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
Study design and conductown dataAll constructs at each time period had strong psychometric properties and met reliability, convergent validity, and discriminant validity criteria.
“All constructs at each time period exhibited strong psychometric properties and satisfied the criteria of reliability and convergent and discriminant validity.”
Quote in the paperSubstantial claimClassified as probably not a substantial claim. May combine two claims.
PremisecitationIncentive alignment is not necessarily organizational rewards; it is a broad concept involving an individual's perception that the IT fits their job requirements and value system.
“According to Ba et al. (2001), incentive alignment does not necessarily mean organizational rewards for using a system.It is a broad concept that entails an individual's perception that the IT fits with his or her job requirements and value system.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ba et al., 2001
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationPLS has few restrictions on distributional assumptions and sample size.
“Chin, Marcolin, and Newsted (2003) noted that PLS has minimal restrictions in terms of distributional assumptions and sample size.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Chin, Marcolin, and Newsted (2003) source
May combine two claims.
Study design and conductown dataMean-centering helps limit multicollinearity, as shown by low VIFs for all constructs in the model.
“Mean-centering helps limit potential multicollinearity, evidenced by the low variation inflation factors (VIFs) for all constructs in our model.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Premisenone givenPerceived usefulness is an instrumental belief, conceptually similar to extrinsic motivation, and is a cognition about the benefits of using a system.
“Perceived usefulness is an instrumental belief that is conceptually similar to extrinsic motivation and is a cognition (as opposed to emotion) regarding the benefits of using a system.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationTAM proposes that perceived usefulness and perceived ease of use mediate the influence of external variables on behavioral intention.
“It further theorizes that the effect of external variables (e.g., design characteristics) on behavioral intention will be mediated by perceived usefulness and perceived ease of use.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Davis et al., 1989 source
May combine two claims.
InterpretationreasoningThe four sites differed in industry, organizational context, functional area, and technology type, and this variation increases the findings' potential generalizability.
“The research sites represented different industries, organizational contexts, and functional areas.Further, the types of ITs were different across the sites.Such variability in organizational settings and types of technologies adds to the potential generalization of our findings.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Study design and conductcitationThe TAM constructs-usefulness, ease of use, and behavioral intention-were measured with items adapted from Davis (1989) and Davis et al. (1989).
“TAM constructs-that is, perceived usefulness (PU), perceived ease of use (PEOU), and behavioral intention (BI)-were operationalized using items adapted from Davis (1989) and Davis et al. (1989).”
Quote in the paperNot a substantial claim0 of 2 sources checkedClassified as probably not a substantial claim.
PremisecitationIncentive alignment has been proposed as the third dimension of systems design, together with software engineering and technology acceptance.
“Incentive alignment has been suggested as the third dimension in systems design (Ba, Stallaert, & Whinston, 2001).The other two dimensions are software engineering and technology acceptance (Ba et al., 2001).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Ba et al., 2001
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
May combine two claims.
Interpretationnone givenModification and enhancement activities performed by peers will increase job relevance, improve output quality, and reduce anxiety related to system use.
“Second, the modification and enhancement activities performed by peers will increase job relevance of a system, improve the output quality of a system, and reduce anxiety related to system use.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
InterpretationreasoningImplementation of interventions is not a silver bullet for greater IT adoption and effective utilization.
“The implementation of interventions is, of course, not a silver bullet for greater IT adoption and effective utilization.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe effect of perceived ease of use on behavioral intention to use a system is stronger for individuals who received game-based training.
“He also found that the effect of perceived ease of use on behavioral intention to use a system was stronger for individuals who received game-based training.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites Venkatesh, 1999
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningIf a rich understanding of determinants and interventions is developed, managers can proactively decide on right interventions to minimize resistance and maximize effective utilization.
“If we can develop a rich understanding of the determinants of IT adoption and use and interventions that can favorably influence these determinants, managers can proactively decide on implementing the right interventions to minimize resistance to new ITs and maximize effective utilization of ITs.”
Quote in the paperNot a substantial claim- Rests on an assumption: ITs are becoming increasingly complex and implementation costs are very high.
- Rests on an assumption: Implementation failures of many of today's ITs cost millions of dollars for organizations.
Classified as probably not a substantial claim.
PremisecitationTraining has been suggested as one of the most important postimplementation interventions leading to greater user acceptance and system success.
“Training has been suggested as one of the most important postimplementation interventions that leads to greater user acceptance and system success (see Sharma & Yetton, 2007).”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Sharma & Yetton, 2007 source
Probably not a substantial claim: may not pass S3 (load-bearing).
InterpretationcitationOrganizational support captures the role of internal and external experts who help users deal with complexity associated with new systems and business processes.
“Organizational support captures the role of both internal and external experts who can help users deal with the complexity associated with new systems as well as business processes.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenImplementation of interventions can substantially increase system development costs.
“Implementation of interventions can increase system development costs substantially.”
Quote in the paperNot a substantial claimMay combine two claims.
Interpretationnone givenTAM3 emphasizes unique roles and processes for perceived usefulness and perceived ease of use and theorizes that determinants of perceived usefulness will not influence perceived ease of use and vice versa.
“TAM3 emphasizes the unique role and processes related to perceived usefulness and perceived ease of use and theorizes that the determinants of perceived usefulness will not influence perceived ease of use and vice versa.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim.
PremisecitationEmployees' perceptions of organizational support, such as facilitating conditions or external control, lead to greater user acceptance of new systems.
“Prior research has suggested that employees' perceptions regarding organizational support-that is, facilitating conditions or perceptions of external control (Taylor & Todd, 1995;Venkatesh, 2000;Venkatesh et al., 2003)-will lead to greater user acceptance of new systems.”
Quote in the paperSubstantial claim0 of 3 sources checkedProbably not a substantial claim: may not pass S2 (checkable), S4 (based).
Interpretationnone givenUnless organizations develop effective interventions to enhance IT adoption and use, there is no practical utility in a rich understanding of IT adoption.
“Our key argument in this article is that unless organizations can develop effective interventions to enhance IT adoption and use, there is no practical utility of our rich understanding of IT adoption.”
Quote in the paperNot a substantial claim- Rests on an assumption: There is little or no scientific research aimed at identifying and linking interventions with specific determinants of IT adoption.
Probably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningInterventions discussed in the article can help organizations generate favorable individual reactions toward business process changes and process standards implementation.
“We suggest that interventions discussed in this article can help organizations generate favorable individuals' reactions toward business process changes and process standards implementation.”
Quote in the paperNot a substantial claimMay combine two claims.
InterpretationreasoningWhile self-efficacy may have a weak influence on perceived usefulness, this influence becomes nonsignificant in the presence of other important social and cognitive constructs.
“For example, while self-efficacy may have weak influence on perceived usefulness as shown in Agarwal and Karahanna (2000), we argue that this influence will become nonsignificant in the presence of other important social and cognitive constructs.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites Agarwal and Karahanna (2000) source
Probably not a substantial claim: may not pass S4 (based).
Interpretationnone givenTAM3 posits that perceptions of external control are important and stable determinants of perceived ease of use.
“TAM3 posits that perceptions of external control are important and stable determinants of perceived ease of use.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
InterpretationreasoningOrganizational support is a key source of perceptions of external control.
“Organizational support is a key source of perceptions of external control.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Interpretationnone givenExperts can help employees modify aspects of a new system, thereby increasing job relevance, output quality, and results demonstrability.
“For example, experts can help employees modify certain aspects of a new system, thus increasing job relevance, output quality, and results demonstrability of a system.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S4 (based).
Interpretationnone givenDue to the complexity of ITs, it is increasingly difficult for employees to make effective decisions about adoption, utilization, and coping with new ITs.
“Due to the complexity of ITs, it is increasingly difficult for employees to make effective decisions about adoption, utilization, and coping with new ITs.”
Quote in the paperNot a substantial claim- Rests on an assumption: ITs are becoming increasingly complex and implementation costs are very high.
May combine two claims.
PremisecitationSome employees react favorably to system-induced changes, while others perceive those changes as a threat to their well-being.
“Some employees may react favorably to these changes, while the others may perceive these changes as a threat to their well-being (Orlikowski, 2000;Boudreau & Robey, 2005).”
Quote in the paperSubstantial claim0 of 2 sources checkedMay combine two claims.
Interpretationnone givenTAM3 posits that with increasing experience, the effect of perceived ease of use on behavioral intention diminishes, while the effect of perceived ease of use on perceived usefulness increases.
“TAM3 posits that with increasing experience, while the effect of perceived ease of use on behavioral intention will diminish, the effect of perceived ease of use on perceived usefulness will increase.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningThe role of training is even more important for complex systems that are central to employees' work life.
“We suggest that the role of training will be even more important in the context of complex systems (e.g., enterprise systems) that are more central to employees' work life.”
Quote in the paperNot a substantial claim- Rests on an assumption: Much prior research on training in the context of IT adoption has been conducted for simple ITs, such as word processing and e-mail.
Classified as probably not a substantial claim. May combine two claims.
InterpretationreasoningComplex systems are more likely to invoke negative reactions from employees because of their disruptive nature, and effective training can mitigate these reactions and help form favorable perceptions.
“As these systems are more likely to invoke negative reactions from employees because of their disruptive nature, effective training interventions can mitigate these negative reactions and help employees form favorable perceptions toward these systems.”
Quote in the paperSubstantial claim
The paper relies on this without support.
Participants who completed all measurement points are representative of the initial user populations at the four sites.
“Out of 50 potential users of the system who participated in the training, 39 provided usable responses at all points of measurement.”
“Out of 51 potential users of the new system who attended the training, 43 provided usable responses at all points of measurement.”
“Out of 51 potential users of the new system, 36 provided usable responses at all points of measurement.”
The paper relies on this without support.
The four sites' data can be pooled for the PLS analysis despite differences in industry, organizational context, and technology.
“Data were collected from four different organizations-sites A through D-implementing new ITs.”
“The research sites represented different industries, organizational contexts, and functional areas.”
The paper relies on this without support.
Management support in individual-level IT adoption literature has been conceptualized and operationalized as organizational mandate and compliance.
“While management support has been conceptualized and operationalized as organizational mandate and compliance, particularly in the individual-level IT adoption literature, we suggest that there is a need to develop a richer conceptualization of management support to enhance our understanding of its role in IT adoption contexts.”
The paper relies on this without support.
There is limited research on the role of incentive alignment in IT adoption contexts.
“There is limited research on the role of incentive alignment in IT adoption contexts.”
The paper relies on this without support.
Much prior research on training in the context of IT adoption has been conducted for simple ITs, such as word processing and e-mail.
“However, much of the prior research on training in the context of IT adoption has been conducted for simple ITs, such as word processing and e-mail.”
The paper relies on this without support.
There is little or no research on the role of peer support in the context of IT adoption.
“While peer support is potentially an important intervention that can lead to greater user acceptance, there is little or no research on the role of peer support in the context of IT adoption.”
The paper relies on this without support.
There is little or no scientific research aimed at identifying and linking interventions with specific determinants of IT adoption.
“However, there is little or no scientific research aimed at identifying and linking interventions with specific determinants of IT adoption.”
The paper relies on this without support.
There have been numerous cases of enterprise system failures because of user resistance.
“There have been numerous cases of enterprise system failures because of user resistance.”
The paper relies on this without support.
In many cases, users stopped using an enterprise system because they saw no benefits of using the new system.
“In many cases, the users stopped using an enterprise system, as they saw no benefits of using the new system.”
The paper relies on this without support.
Other types of reactions, such as avoidance, sabotage, workarounds, and shortcuts, are prevalent.
“Other types of reactions, such as avoidance, sabotage, workarounds, and shortcuts are also prevalent.”
The paper relies on this without support.
ITs are becoming increasingly complex and implementation costs are very high.
“ITs are becoming increasingly complex and implementation costs are very high.”
The paper relies on this without support.
Implementation failures of many of today's ITs cost millions of dollars for organizations.
“Implementation failures of many of today's ITs cost millions of dollars for organizations.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
TAM's extensive focus on technology to the neglect of social and psychological parameters limits its explanatory and predictive utilities.
“the extensive focus of TAM on technology to the neglect of social and psychological parameters (Agarwal & Prasad, 1998;Venkatesh & Bala, 2008) limits its explanatory and predictive utilities, and therefore demands its integration with other frameworks”
From Using T-O-E theoretical framework to study the adoption of ERP solution (2016) · cites this paper as Venkatesh & Bala, 2008
The introductory discussion describes TAM as “highly predictive of IT adoption and use,” contrary to the claim that its predictive utility is limited. It identifies TAM’s commonly cited limitation as a lack of actionable guidance, not a neglect of social and psychological factors; the Theoretical Framework discusses social influence and individual differences in extensions of TAM. No page numbers are provided in our copy. (LLM, conf=0.86)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredA technology that provides information quickly and easily will lead to improvements in job performance, which in turn will encourage greater use of a given technology.
“A technology that provides information quickly and easily will lead to improvements in job performance, which in turn will encourage greater use of a given technology (Venkatesh and Bala, 2008).”
From Examining the antecedents of the technology acceptance model within e-procurement (2018) · cites this paper as Venkatesh and Bala, 2008
The Design characteristics section says timely, understandable information may help users make better decisions and that information-related design can improve productivity and performance. The Explaining and Predicting Behavioral Intention and Use section finds that perceived usefulness predicts intention, which predicts use. The claim overstates these links as a guaranteed sequence from quick, easy information to improved job performance to greater use; the paper does not establish that full causal chain. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe adoption of new technologies is shaped by user perceptions and usage patterns as well as student characteristics and experiences.
“The adoption of new technologies is shaped by user perceptions and usage patterns (Venkatesh & Bala, 2008) as well as student characteristics and experiences (Abdaljaleel et al., 2023; Tiwari et al., 2024).”
From “ChatGPT seems too good to be true”: College students’ use and perceptions of generative AI (2024) · cites this paper as Venkatesh & Bala, 2008
Perceived enjoyment is positively related to perceived ease of use.
“It is proposed that perceived enjoyment is positively related to perceived ease of use [12].”
From Gender and Acceptance of E-Learning: A Multi-Group Analysis Based on a Structural Equation Model among College Students in Chile and Spain (2015) · cites this paper as [12]
The scales used in this study have been verified in preceding studies.
“The scales applied have been verified in preceding studies [12, 28].”
From Gender and Acceptance of E-Learning: A Multi-Group Analysis Based on a Structural Equation Model among College Students in Chile and Spain (2015) · cites this paper as [12]
Perception of external control is an element that conditions perceived ease of use.
“Moreover, it is proposed that perception of external control is an element that conditions perceived ease of use [12].”
From Gender and Acceptance of E-Learning: A Multi-Group Analysis Based on a Structural Equation Model among College Students in Chile and Spain (2015) · cites this paper as [12]
Result demonstrability is an antecedent of perceived usefulness.
“It is proposed that result demonstrability is an antecedent of perceived usefulness [12].”
From Gender and Acceptance of E-Learning: A Multi-Group Analysis Based on a Structural Equation Model among College Students in Chile and Spain (2015) · cites this paper as [12]
The Technology Acceptance Model explains that users' decisions to accept and use a technology are influenced by perceived usefulness, ease of use, and social influence of others.
“which explains that users’ decisions to accept and use a technology are influenced by perceived usefulness, ease of use, and social influence of others.”
From Barriers to and facilitators of user engagement with digital mental health interventions: Systematic review (2021) · cites this paper as [253]
Some themes identified in this review overlap with previous models conceptualizing engagement with digital health interventions and general technology acceptance and health behavior.
“Some of the themes identified in this review overlap with previous models conceptualizing engagement with digital health interventions, as well as general technology acceptance and health behavior, such as the Efficiency Model of Support [251], Technology Acceptance Model [253], and Health Belief Model [249].”
From Barriers to and facilitators of user engagement with digital mental health interventions: Systematic review (2021) · cites this paper as [253]
Our findings are in line with the Technology Acceptance Model.
“Our findings are in line with the Technology Acceptance Model”
From Barriers to and facilitators of user engagement with digital mental health interventions: Systematic review (2021) · cites this paper as [253]
Information-related characteristics of a system can impact its perceived usefulness as they can improve productivity and performance.
“Information-related characteristics of a system can impact its PU as they can improve productivity and performance (Venkatesh and Bala, 2008).”
From Examining the antecedents of the technology acceptance model within e-procurement (2018) · cites this paper as Venkatesh and Bala, 2008
A reliable, flexible and user-friendly system is more likely to create a positive user experience.
“Firstly, considering usability , a reliable, flexible and user-friendly system is more likely to create a positive user experience (Venkatesh and Bala 2008).”
From Examining the antecedents of the technology acceptance model within e-procurement (2018) · cites this paper as Venkatesh and Bala 2008
A typical criticism of the TAM is its lack of practical managerial guidance.
“A typical criticism of the TAM is in relation to its lack of practical managerial guidance (Venkatesh and Bala, 2008).”
From Examining the antecedents of the technology acceptance model within e-procurement (2018) · cites this paper as Venkatesh and Bala, 2008
There are four generic antecedents to the TAM: individual differences, system characteristics, social influence, and facilitating conditions.
“Venkatesh and Bala (2008) present four generic antecedents to the TAM: i ndividual differences , system characteristics , social influence , and facilitating conditions .”
From Examining the antecedents of the technology acceptance model within e-procurement (2018) · cites this paper as Venkatesh and Bala (2008)
The Technology Acceptance Model explains approximately 40% of the variance in individuals' intention to use a new technology and actual use.
“The model explains approximately 40% of the variance in individuals’ intention to use a new technology and actual use [39].”
From An Assessment of How Clinicians and Staff Members Use a Diabetes Artificial Intelligence Prediction Tool: Mixed Methods Study (2023) · cites this paper as [39]
The Technology Acceptance Model theorizes that individuals' intention to use new technology is determined by perceived usefulness and perceived ease of use.
“It theorizes that individuals’ intention to use new technology is determined by perceived usefulness, defined as “the extent to which a person believes that using [a new technology] will enhance his or her job performance,” and perceived ease of use, defined as “the degree to which a person believes that using [a new technology] will be free of effort” [39].”
From An Assessment of How Clinicians and Staff Members Use a Diabetes Artificial Intelligence Prediction Tool: Mixed Methods Study (2023) · cites this paper as [39]
Perceived usefulness is a primary determinant of technology acceptance and subsequent usage behaviors.
“TAM's assertion that perceived usefulness is a primary determinant of technology acceptance and subsequent usage behaviors (Venkatesh & Bala, 2008).”
From The impact of AI-assisted pair programming on student motivation, programming anxiety, collaborative learning, and programming performance: a comparative study with traditional pair programming and individual approaches (2025) · cites this paper as Venkatesh & Bala, 2008
The technology acceptance model models user's acceptance of technology mainly based on perceived usefulness and perceived ease of use.
“Currently, many studies contributing to the adoption research of mHealth for youths are based on the technology acceptance model, an information systems theory that models user’s acceptance of technology mainly based on perceived usefulness and perceived ease of use [48-50].”
From Understanding Mobile Health and Youth Mental Health: Scoping Review (2023) · cites this paper as [50]
Many studies contributing to the adoption research of mHealth for youths are based on the technology acceptance model.
“Currently, many studies contributing to the adoption research of mHealth for youths are based on the technology acceptance model, an information systems theory that models user’s acceptance of technology mainly based on perceived usefulness and perceived ease of use [48-50].”
From Understanding Mobile Health and Youth Mental Health: Scoping Review (2023) · cites this paper as [50]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
T2-T3 interval is misstated
MinorThe paper says T2 and T3 were separated by three months, but it schedules them one and three months after implementation, respectively-a two-month interval.
“Specifically the measurements of use and its determinants were separated by 1 month (T1-T2), 3 months (T2-T3) and 2 months (T3-T4).”
“after initial training (T1), 1 month after implementation (T2), and 3 months after implementation (T3).We also measured self-reported usage at T2, T3, and 5 months after implementation (T4).”
Use measure conflicts with the stated response scale
MinorThe appendix says all items except computer self-efficacy used a seven-point agreement scale, yet its use item asks for time spent on the system each day.
“a All items were measured on a 7-point Likert scale (where 1: strongly disagree; 2: moderately disagree, 3: somewhat disagree, 4: neutral (neither disagree nor agree), 5: somewhat agree, 6: moderately agree, and 7: strongly agree), except computer self-efficacy, which was measured using a 10-point Guttman scale.”
“Use (USE) USE1 On average, how much time do you spend on the system each day?”
Voluntariness interaction is mislabeled
MinorThe text calls the subjective-norm-by-voluntariness interaction "SN × EXP," but Table 7 labels that interaction "SN × VOL"; EXP denotes experience.
“The two-way interaction between subjective norm and voluntariness (SN × EXP) indicated that the effect of subjective norm on behavioral intention was stronger in a mandatory context.”
“| SN × VOL | .29 * * * | .22 * * * | .17 * | .03 |”
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Research Note. Electronic Brainstorming: Illusions and Patterns of Productivity
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Isolating Attitudinal and Normative Influences in Behavioral Intentions Models
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Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead
University of Arkansas, Venkatesh, V., Thong, J., Hong Kong University of Science and Technology, Xu, X., & The Hong Kong Polytechnic University (2016) · Journal of the Association for Information Systems · doi:10.17705/1jais.00428 · Cited by 2,513 · see citing works
“Note that we did not count studies that integrated TAM (Davis, 1989) and its updates (e.g., TAM3: Venkatesh & Bala, 2008) in this category but as general citations.”
BackgroundMethodInfluentialSelf-citationDwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019) · Information Systems Frontiers · doi:10.1007/s10796-017-9774-y · Cited by 2,137 · see citing works
Scherer, R., Siddiq, F., & Tondeur, J. (2019) · Computers & Education · doi:10.1016/j.compedu.2018.09.009 · Cited by 1,923 · see citing works
“Venkatesh and Bala (2008), therefore, classi fi ed the possible relevance of the TAM model into two categories: pre-implementation and post-implementation phases.”
BackgroundInfluentialEvaluating E-learning systems success: An empirical study
Al-Fraihat, D., Joy, M., Masa'deh, R., & Sinclair, J. (2020) · Computers in Human Behavior · doi:10.1016/j.chb.2019.08.004 · Cited by 1,350 · see citing works
Borghouts, J., Eikey, E., Mark, G., De Leon, C., Schueller, S. M., Schneider, M., Stadnick, N., Zheng, K., Mukamel, D., & Sorkin, D. H. (2021) · Journal of Medical Internet Research · doi:10.2196/24387 · Cited by 1,069 · see citing works
“…themes identified in this review overlap with previous models conceptualizing engagement with digital health interventions, as well as general technology acceptance and health behavior, such as the Efficiency Model of Support [251], Technology Acceptance Model [253], and Health Belief Model [249].”
Backgroundde Queiroz, M. M., & Wamba, S. F. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.11.021 · Cited by 1,150 · see citing works
The literature review of technology adoption models and theories for the novelty technology
Lai, P. C. (2017) · Journal of Information Systems and Technology Management · doi:10.4301/s1807-17752017000100002 · Cited by 1,135 · see citing works
Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C. (2020) · Telematics and Informatics · doi:10.1016/j.tele.2020.101473 · Cited by 1,100 · see citing works
Trust in AI and Its Role in the Acceptance of AI Technologies
Choung, H., David, P., & Ross, A. (2023) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2022.2050543 · Cited by 963 · see citing works
“Later, more elaborate versions of the TAM (i.e., TAM 2, TAM 3) were developed, incorporating additional external factors, such as social norms and perceived enjoyment (Venkatesh & Bala, 2008; Venkatesh & Davis, 2000), and treating attitude as a predictor of behavior.”
BackgroundVenkatesh, V., Thong, J. Y. L., Chan, F. K. Y., Hu, P. J.-H., & Brown, S. A. (2011) · Information Systems Journal · doi:10.1111/j.1365-2575.2011.00373.x · Cited by 852 · see citing works
“This extension is in line with recent calls for incorporating constructs relevant to the changing nature of emerging technologies to aid the design of interventions (Venkatesh et al ., 2007; Venkatesh & Bala, 2008).”
ResultSelf-citationPatil, P. P., Tamilmani, K., Rana, N. P., & Raghavan, V. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2020.102144 · Cited by 933 · see citing works
Makransky, G., & Lilleholt, L. (2018) · Educational Technology Research and Development · doi:10.1007/s11423-018-9581-2 · Cited by 796 · see citing works
“According to the technology acceptance model (TAM), behavioral intention to use predicts actual use (Venkatesh and Bala 2008).”
BackgroundMethodInfluentialMcLean, G., & Osei-Frimpong, K. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.05.009 · Cited by 762 · see citing works
(2012) · DSpace (Open University in the Netherlands) · Cited by 754 · see citing works
Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model
Chatterjee, S., Rana, N. P., Dwivedi, Y. K., & Baabdullah, A. M. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.120880 · Cited by 825 · see citing works
Abdullah, F., Ward, R., & Ahmed, E. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.05.014 · Cited by 693 · see citing works
“(Venkatesh & Bala, 2008, p.314; Venkatesh & Davis, 2000) BI2 Given that I had access to the e-portfolio, I predict that I would use it.”
BackgroundA Systematic Review of the Technology Acceptance Model in Health Informatics
Rahimi, B., Nadri, H., Lotfnezhad Afshar, H., & Timpka, T. (2018) · Applied Clinical Informatics · doi:10.1055/s-0038-1668091 · Cited by 688 · see citing works
Adoption and use of AI tools: a research agenda grounded in UTAUT
Venkatesh, V. (2021) · Annals of Operations Research · doi:10.1007/s10479-020-03918-9 · Cited by 783 · see citing works
Self-citationMcLean, G., & Wilson, A. M. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2019.07.002 · Cited by 726 · see citing works
Salloum, S. A., Qasim Mohammad Alhamad, A., Al-Emran, M., Abdel Monem, A., & Shaalan, K. (2019) · IEEE Access · doi:10.1109/access.2019.2939467 · Cited by 651 · see citing works
“The term signifies the intrinsic motivation factor which is related to the use of a new system [64].”
BackgroundWhen predictors of outcomes are necessary: guidelines for the combined use of PLS-SEM and NCA
Richter, N., Schubring, S., Hauff, S., Ringle, C. M., & Sarstedt, M. (2020) · Industrial Management & Data Systems · doi:10.1108/imds-11-2019-0638 · Cited by 667 · see citing works
Fernandes, T., & Oliveira, E. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.08.058 · Cited by 630 · see citing works
Trenerry, B., Chng, S., Wang, Y., Suhaila, Z. S., Lim, S. S., Lu, H. Y., & Oh, P. H. (2021) · Frontiers in Psychology · doi:10.3389/fpsyg.2021.620766 · Cited by 766 · see citing works
Zhong-qing, H., Ding, S., Li, S., Chen, L., & Yang, S. (2019) · Symmetry · doi:10.3390/sym11030340 · Cited by 677 · see citing works
“A large number of empirical studies on the adoption of information technology in the past decade have shown that perceived usefulness can have a positive impact on users’ intentions [22–25].”
BackgroundPredicting collaboration technology use: integrating technology adoption and collaboration research
Brown, S. A., Dennis, A. R., & Venkatesh, V. (2010) · Journal of Management Information Systems · doi:10.2753/mis0742-1222270201 · Cited by 592 · see citing works
“Effort expectancy can be particularly important in the context of personal technologies and non-workplace settings [70] and has been identified as an important predictor in the context of communication technologies [61].”
BackgroundInfluentialSelf-citationAlmost human? A comparative case study on the social media presence of virtual influencers
Arsenyan, J., & Mirowska, A. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2021.102694 · Cited by 566 · see citing works
Morosan, C. V., & Defranco, A. L. (2015) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2015.11.003 · Cited by 576 · see citing works
Aubert, B. A., Schroeder, A., & Grimaudo, J. (2012) · Decision Support Systems · doi:10.1016/j.dss.2012.07.002 · Cited by 573 · see citing works
“Finally, the perception of the usefulness of a technology is influenced by the extent to which a technology is seen as easy to use [18,70].”
BackgroundA hedonic motivation model in virtual reality tourism: Comparing visitors and non-visitors
Kim, M. J., & Hall, C. M. (2019) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2018.11.016 · Cited by 560 · see citing works
Hansen, J. M., Saridakis, G., & Benson, V. (2017) · Computers in Human Behavior · doi:10.1016/j.chb.2017.11.010 · Cited by 568 · see citing works
“Further research has been undertake to try to improve upon its accuracy and predictive ability (e.g., Szajna, 1996; Venkatesh & Bala, 2008; Venkatesh & Davis, 2000; Venkatesh, Morris, Davis, & Davis, 2003; Wixom & Todd, 2005).”
Backgroundde Luna, I. R., Liébana-Cabanillas, F. J., Sánchez‐Fernández, J., & Muñoz‐Leiva, F. (2018) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2018.09.018 · Cited by 564 · see citing works
The impact of trust and perceived risk on internet banking adoption in India
Kesharwani, A., & Bisht, S. S. (2012) · International Journal of Bank Marketing · doi:10.1108/02652321211236923 · Cited by 543 · see citing works
“Many researchers have empirically proven that perceived usefulness exert a significant and positive effect on attitude towards using IT or associated systems (Venkatesh and Bala, 2008; Davis, 1989; Venkatesh and Davis, 2000).”
BackgroundA theoretical model of mobile augmented reality acceptance in urban heritage tourism
Dieck, M. C. T., & Jung, T. H. (2015) · Current Issues in Tourism · doi:10.1080/13683500.2015.1070801 · Cited by 542 · see citing works
“Within previous TAM research, a number of researchers (Lucas & Spitler, 1999; Pai & Huang, 2011; Venkatesh & Bala, 2008) con-fi rmed the importance of information quality for perceived usefulness and perceived ease of use.”
BackgroundWearable technologies: The role of usefulness and visibility in smartwatch adoption
Chuah, S. H., Rauschnabel, P. A., Krey, N., Nguyen, B., Ramayah, T., & Lade, S. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.07.047 · Cited by 546 · see citing works
“Visibility is distinct but related to the image construct and the norms construct in prior TAM research (Fisher & Price, 1992; Venkatesh & Bala, 2008; Venkatesh & Davis, 2000).”
BackgroundAntecedents of the adoption of the new mobile payment systems: The moderating effect of age
Liébana-Cabanillas, F. J., Sánchez‐Fernández, J., & Muñoz‐Leiva, F. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.03.022 · Cited by 523 · see citing works
“…2000) with the inclusion of social influence processes and instrumental cognitive processes, UTAUT (Venkatesh, Morris, Davis, & Davis, 2003), TAM 3 (Venkatesh & Bala, 2008), which expand the primitive model with a set of predictive and moderating factors, and the MOPTAM (Renaud and Biljon’s Mobile…”
BackgroundMethodInfluentialAssessing Common Method Bias: Problems with the ULMC Technique1
Chin, W. W., Thatcher, J. B., & Wright, R. T. (2012) · MIS Quarterly · doi:10.2307/41703491 · Cited by 505 · see citing works
“…al. 2010; Sarker and Valacich 2010; Seddon et al. 2010; Setia et al. 2011; Sharma et al. 2009; Shi et al. 2008; Shim et al. 2009; Vance et al. 2008; Venkatesh and Bala 2008; Weerakkody et al. 2009; Wong et al. 2009; Xue et al. 2008; Xue et al. 2011; Yamouchia and Swanson 2010; Zhu et al. 2010 43…”
BackgroundExploring influencing variables for the acceptance of social robots
de Graaf, M. M. A., & Ben Allouch, S. (2013) · Robotics and Autonomous Systems · doi:10.1016/j.robot.2013.07.007 · Cited by 499 · see citing works
“Behavioral control Bandura [56] 10 10 4.”
Determinants of intention to use the mobile banking apps: An extension of the classic TAM model
Muñoz‐Leiva, F., Climent-Climent, S., & Liébana-Cabanillas, F. J. (2017) · Spanish Journal of Marketing - ESIC · doi:10.1016/j.sjme.2016.12.001 · Cited by 556 · see citing works
Chang, C., Hajiyev, J., & Su, C.-R. (2017) · Computers & Education · doi:10.1016/j.compedu.2017.04.010 · Cited by 479 · see citing works
Kim, J., & Park, H.-A. (2012) · Journal of Medical Internet Research · doi:10.2196/jmir.2143 · Cited by 475 · see citing works
“TAM has been continuously expanded to TAM2, the unified theory of acceptance and use of technology, and TAM3, and each of these expansions was motivated by the need to predict the usability of new information technology and to identify and stimulate the use of the technology [9-12].”
BackgroundEmpirical investigation of e-learning acceptance and assimilation: A structural equation model
Al‐Gahtani, S. S. (2014) · Applied Computing and Informatics · doi:10.1016/j.aci.2014.09.001 · Cited by 493 · see citing works
“According to Venkatesh and Bala [44], ‘‘individual difference variables include personality and/or demo-graphics (e.g., traits or states of individuals, gender, and age) that can influence individuals’ perceptions of PU and PEOU’’, while, ‘‘system characteristics are those salient features of a…”
BackgroundMethodInfluentialRese, A., Baier, D., Geyer-Schulz, A., & Schreiber, S. (2016) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2016.10.010 · Cited by 500 · see citing works
“Thus, the TAM model has been extended with various constructs, e.g. the TAM2 model (Venkatesh and Davis, 2000) adds antecedents to PU and the TAM3 model (Venkatesh and Bala, 2008) to PEOU.”
MethodAnalysis of users and non-users of smartphone applications
Verkasalo, H., López‐Nicolás, C., Molina‐Castillo, F., & Bouwman, H. (2009) · Telematics and Informatics · doi:10.1016/j.tele.2009.11.001 · Cited by 469 · see citing works
“Social norms, also referred to as subjective norms or social influence, represent a factor that is assumed to have a direct impact on perceived utility (López-Nicolás et al., 2008; Sun and Zhang, 2006; Venkatesh and Bala, 2008).”
BackgroundMethodChopdar, P. K., Korfiatis, N., Sivakumar, V. J., & Lytras, M. D. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.04.017 · Cited by 468 · see citing works
Albayati, H., Kim, S. K., & Rho, J. J. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101320 · Cited by 464 · see citing works
Predicting m-commerce adoption determinants: A neural network approach
Chong, A. Y. (2012) · Expert Systems with Applications · doi:10.1016/j.eswa.2012.07.068 · Cited by 454 · see citing works
Miltgen, C. L., Popovič, A., & Oliveira, T. (2013) · Decision Support Systems · doi:10.1016/j.dss.2013.05.010 · Cited by 460 · see citing works
“Developing theory that is more focused and context specific – here, technology specific – is considered an important frontier for advances in IS research [68, 84].”
BackgroundInfluentialDiscovering Unobserved Heterogeneity in Structural Equation Models to Avert Validity Threats1
Becker, J.-M., Rai, A., Ringle, C. M., & Völckner, F. (2013) · MIS Quarterly · doi:10.25300/misq/2013/37.3.01 · Cited by 464 · see citing works
“…existing theory expects contextual variables, such as voluntariness or task type (e.g., Venkatesh and Davis 2000), or psychographic variables, such as personal innovativeness and computer attitude, to cause heterogeneity in the relationships among the TAM constructs (e.g., Venkatesh and Bala 2008).”
BackgroundMeléndez, A. P., Obra, A. R. D. Á., & Garrido‐Moreno, A. (2012) · Computers & Education · doi:10.1016/j.compedu.2012.12.014 · Cited by 456 · see citing works
Taking “Fun and Games” Seriously: Proposing the Hedonic-Motivation System Adoption Model (HMSAM)
Lowry, P. B., Gaskin, J. E., Twyman, N. W., Hammer, B. I., & Roberts, T. (2013) · Journal of the Association for Information Systems · doi:10.17705/1jais.00347 · Cited by 473 · see citing works
“It is by illuminating alternative theoretical perspectives and providing evidence for important theoretical boundaries that system acceptance studies continue to make meaningful contributions (Venkatesh & Bala, 2008; Venkatesh, Davis, & Morris, 2007).”
BackgroundTarhini, A., Hone, K., Liu, X., & Tarhini, T. (2017) · Interactive Learning Environments · doi:10.1080/10494820.2015.1122635 · Cited by 445 · see citing works
“Many studies have supported TAM ’ s explanatory power within the context of IS implementation in general (Venkatesh & Bala, 2008) and a number of studies have successfully extended its application to the context of e-learning (Alshare, Freeze, Lane, & Wen, 2011; Chang & Tung, 2008; Hu & Hui, 2012;…”
BackgroundA conceptual model for the process of IT innovation adoption in organizations
Hameed, M. A., Counsell, S., & Swift, S. (2012) · Journal of Engineering and Technology Management · doi:10.1016/j.jengtecman.2012.03.007 · Cited by 442 · see citing works
Al-Rahmi, W. M., Yahaya, N., Aldraiweesh, A. A., Alamri, M. M., Aljarboa, N. A., Alturki, U., & Aljeraiwi, A. A. (2019) · IEEE Access · doi:10.1109/access.2019.2899368 · Cited by 435 · see citing works
Xu, D. J., Benbasat, I., & Cenfetelli, R. T. (2013) · MIS Quarterly · doi:10.25300/misq/2013/37.3.05 · Cited by 431 · see citing works
Faqih, K. M. S., & Jaradat, M.-I. R. M. (2014) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2014.09.006 · Cited by 421 · see citing works
“Meanwhile, this is an early adoption study, therefore we delete from the original TAM3 post implementation variables (perceived enjoyment and objective usability) which play a role in determining perceived ease of use after individuals gain experience with the new system (Venkatesh and Bala, 2008).”
BackgroundMethodInfluentialSukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & Hakim, H. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e05410 · Cited by 443 · see citing works
“Studies reported some external factors to accompany the original TAM constructs (Venkatesh and Bala, 2008; Venkatesh and Davis, 2000).”
Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis
Zhang, C., Schießl, J., Plößl, L., Hofmann, F., & Gläser-Zikuda, M. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00420-7 · Cited by 413 · see citing works
Consumer Resistance to Innovation in Services: Challenges and Barriers in the Internet of Things Era
Mani, Z., & Chouk, I. (2018) · Journal of Product Innovation Management · doi:10.1111/jpim.12463 · Cited by 405 · see citing works
Brown, B. C., Gude, W. T., Blakeman, T., van der Veer, S. N., Ivers, N. M., Francis, J., Lorencatto, F., Presseau, J., Peek, N. B., & Daker‐White, G. (2019) · Implementation Science · doi:10.1186/s13012-019-0883-5 · Cited by 394 · see citing works
“Technology Acceptance Model [66] Variables: Feedback attitude, Relevance, Usability Mechanisms: Relative advantage”
BackgroundCloud computing adoption and usage in community colleges
Behrend, T. S., Wiebe, E., London, J., & Johnson, E. C. (2010) · Behaviour and Information Technology · doi:10.1080/0144929x.2010.489118 · Cited by 383 · see citing works
“That is, more experience solidifies one’s perception of ease of use and increases its effect on perceived usefulness (Venkatesh and Bala 2008).”
BackgroundInfluentialOleson, A. K., & Hora, M. T. (2013) · Higher Education · doi:10.1007/s10734-013-9678-9 · Cited by 399 · see citing works
“September 2012”
BackgroundKowalczuk, P., Siepmann, C., & Adler, J. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.10.050 · Cited by 401 · see citing works
Mailizar, M., Burg, D., & Maulina, S. (2021) · Education and Information Technologies · doi:10.1007/s10639-021-10557-5 · Cited by 420 · see citing works
“Furthermore, for the other constructs, we adopted from (Al-Adwan et al., 2013; Davis, 1989; Venkatesh & Bala, 2008).”
MethodTechnology Acceptance in Healthcare: A Systematic Review
AlQudah, A. A., Al‐Emran, M., & Shaalan, K. F. (2021) · Applied Sciences · doi:10.3390/app112210537 · Cited by 404 · see citing works
“Those theories include but are not limited to the theory of reasoned action (TRA) [12], the technology acceptance model (TAM) [13–15], extensions of TAM [16,17], the unified theory of acceptance and use of technology (UTAUT) [18], social cognitive theory (SCT) [19,20], the theory of interpersonal behavior (TIB) [21],…”
BackgroundGansser, O. A., & Reich, C. S. (2021) · Technology in Society · doi:10.1016/j.techsoc.2021.101535 · Cited by 398 · see citing works
Mobile-banking adoption: empirical evidence from the banking sector in Pakistan
Farah, M. F., Hasni, M. J. S., & Abbas, A. K. (2018) · International Journal of Bank Marketing · doi:10.1108/ijbm-10-2017-0215 · Cited by 361 · see citing works
Robotics for Customer Service: A Useful Complement or an Ultimate Substitute?
Xiao, L., & Kumar, V. (2019) · Journal of Service Research · doi:10.1177/1094670519878881 · Cited by 366 · see citing works
“Literature on TAM indicates that the characteristics of both customers and service robots will have an effect on customers’ behavior of adoption and interaction with service robots (e.g., Venkatesh and Bala 2008; Venkatesh et al. 2003).”
BackgroundInfluentialMcLean, G., Osei-Frimpong, K., & Barhorst, J. B. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.11.045 · Cited by 381 · see citing works
Nastjuk, I., Herrenkind, B., Marrone, M., Brendel, B., & Kolbe, L. M. (2020) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.120319 · Cited by 360 · see citing works
Venkatesh, V., Thong, J. Y. L., Chan, F. K. Y., & Hu, P. J. (2016) · Information Systems Research · doi:10.1287/isre.2015.0612 · Cited by 364 · see citing works
“…and use of e-government services, some research has noted that findings concerning the general factors in these models (e.g., perceived usefulness, perceived ease of use) cannot provide specific guidance to direct design and practice (Hong et al. 2014, Venkatesh 2000, Venkatesh and Bala 2008).”
BackgroundSelf-citationThe role of task-technology fit as users’ motivation to continue information system use
Larsen, T. J., Sørebø, A. M., & Sørebø, Ø. (2009) · Computers in Human Behavior · doi:10.1016/j.chb.2009.02.006 · Cited by 352 · see citing works
“Over the last two decades, the amount of research within the TAM umbrella has been vigorous (Venkatesh, 2008).”
BackgroundFactors affecting the adoption and use of urban air mobility
Haddad, C. A., Chaniotakis, E. M., Straubinger, A., Plötner, K., & Antoniou, C. (2019) · Transportation Research Part A Policy and Practice · doi:10.1016/j.tra.2019.12.020 · Cited by 351 · see citing works
Predicting the intention to use mobile banking in India
Singh, S., & Srivastava, R. K. (2018) · International Journal of Bank Marketing · doi:10.1108/ijbm-12-2016-0186 · Cited by 377 · see citing works
“(Venkatesh & Bala, 2008), TPB (57”
de Queiroz, M. M., Wamba, S. F., de Bourmont, M., & Telles, R. (2020) · International Journal of Production Research · doi:10.1080/00207543.2020.1803511 · Cited by 367 · see citing works
“The literature concerning technology adoption models is rich (Davis 1989; Venkatesh and Davis 2000; Venkatesh et al. 2003; Venkatesh and Bala 2008; Venkatesh, Thong, and Xu 2012).”
BackgroundHolmes, T. C., Singh, R., Axt, M., Brenner, S., & Barteit, S. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00700-y · Cited by 397 · see citing works
“In general, technology acceptance is a complex process 48 .”
BackgroundIntegrated technology-organization-environment (T-O-E) taxonomies for technology adoption
Awa, H. O., Ojiabo, O. U., & Orokor, L. E. (2017) · Journal of Enterprise Information Management · doi:10.1108/jeim-03-2016-0079 · Cited by 378 · see citing works
Pynoo, B., Devolder, P., Tondeur, J., van Braak, J., Duyck, W., & Duyck, P. (2010) · Computers in Human Behavior · doi:10.1016/j.chb.2010.10.005 · Cited by 341 · see citing works
Zhang, P. (2013) · MIS Quarterly · doi:10.25300/misq/2013/37.1.11 · Cited by 331 · see citing works
“…Tam 2006; Hwang and Kim 2007; Igbaria et al. 1996; Lin et al. 2005; Parboteeah et al. 2009; Thong et al. 2006; van der Heijden 2004; Venkatesh 2000; Venkatesh and Bala 2008; Yi and Hwang 2003 The extent to which the activity of using the computer is perceived to be enjoyable in its own right,…”
BackgroundBehavioural intention to adopt mobile wallet: a developing country perspective
Madan, K., & Yadav, R. (2016) · Journal of Indian Business Research · doi:10.1108/jibr-10-2015-0112 · Cited by 327 · see citing works
Wang, J., & Fan, W. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-04787-y · Cited by 322 · see citing works
“The technology acceptance model posits that perceived usefulness and perceived ease of use in fl uence an individual ’ s intention or attitude toward using new technology (Venkatesh and Bala, 2008).”
RetractedLim, S. H., Kim, D. J., Hur, Y., & Park, K. (2018) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2018.1507132 · Cited by 354 · see citing works
“…behaviors in information technology, the usefulness of emerging technologies (e.g. IoT), the factors that affect the acceptance behavior, etc. (Davis et al., 1989; Venkatesh, 2000; Venkatesh & Bala, 2008; Venkatesh & Davis, 2000; Venkatesh et al., 2003; Yang, Lu, Gupta, Cao, & Zhang, 2012).”
BackgroundMe, My Self, and I(T): Conceptualizing Information Technology Identity and ITS Implications1
Carter, M., & Grover, V. (2015) · MIS Quarterly · doi:10.25300/misq/2015/39.4.9 · Cited by 324 · see citing works
Technology Adoption by Older Adults: Findings From the PRISM Trial
Mitzner, T. L., Savla, J., Boot, W. R., Sharit, J., Charness, N., Czaja, S. J., & Rogers, W. A. (2019) · The Gerontologist · doi:10.1093/geront/gny113 · Cited by 311 · see citing works
Predictors of customer acceptance of and resistance to smart technologies in the retail sector
Roy, S. K., Balaji, M. S., Quazi, A. M., & Quaddus, M. (2018) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2018.02.005 · Cited by 336 · see citing works
“…system characteristics, individual differences, facilitating conditions, consumer traits and organisation characteristics as exogenous variables of the TAM model (Venkatesh and Bala, 2008; Kwee-Meier et al., 2016; Gelderman et al., 2011; Rosenbaum and Wong, 2015; Purohit and Srivastava, 2001).”
BackgroundAgudo-Peregrina, Á. F., Hernández-García, Á., & Pascual-Miguel, F. J. (2013) · Computers in Human Behavior · doi:10.1016/j.chb.2013.10.035 · Cited by 325 · see citing works
Pookulangara, S., & Koesler, K. (2011) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2011.03.003 · Cited by 326 · see citing works
Information and Communication Technologies in Tourism 2014
Xiang, Z., & Tussyadiah, I. P. (2013) · Springer eBooks · doi:10.1007/978-3-319-03973-2 · Cited by 337 · see citing works
Factors influencing students’ adoption of e-learning: a structural equation modeling approach
Tarhini, A., Masa’deh, R. M. T., Al‐Busaidi, K. A., Mohammed, A. B., & Maqableh, M. (2017) · Journal of International Education in Business · doi:10.1108/jieb-09-2016-0032 · Cited by 318 · see citing works
Sands, S., Ferraro, C., Demsar, V., & Chandler, G. (2022) · Business Horizons · doi:10.1016/j.bushor.2022.08.002 · Cited by 337 · see citing works
Factors Affecting Mobile Payment Adoption Intention: An Indian Perspective
Shankar, A., & Datta, B. (2018) · Global Business Review · doi:10.1177/0972150918757870 · Cited by 325 · see citing works
“The TAM is widely adopted and robust model in information technology adoption-related studies (Arvidsson, 2014; Chen, 2008; Duane et al., 2014; Keramati et al., 2012; Kim et al., 2010; Venkatesh & Bala, 2008; Venkatesh & Davis, 2000; Yan, Md-Nor, Abu-Shanab & Sutanonpaiboon, 2009).”
MethodCan teachers’ digital competence influence technology acceptance in vocational education?
Antonietti, C., Cattáneo, A., & Amenduni, F. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107266 · Cited by 467 · see citing works
Vahdat, A., Alizadeh, A., Quach, S., & Hamelin, N. (2020) · Australasian Marketing Journal (AMJ) · doi:10.1016/j.ausmj.2020.01.002 · Cited by 310 · see citing works
“The second group concentrates on factors as intermediaries (such as satisfaction and stickiness between TAM factors and the dependant variable), while the third group encompasses studies like TAM2 (Venkatesh and Davis, 2000) and TAM3 (Venkatesh and Bala, 2008), which propose factors anticipating perceived usefulness a…”
BackgroundMobile payments adoption by US consumers: an extended TAM
Bailey, A. A., Pentina, I., Mishra, A. S., & Mimoun, M. S. B. (2017) · International Journal of Retail & Distribution Management · doi:10.1108/ijrdm-08-2016-0144 · Cited by 309 · see citing works
Hertzum (2017) · doi:10.1145/3127358 · Cited by 307 · see citing works
“Consequently, several later TAM studies recommend the incorporation of subjective norm (e.g., Taylor & Todd, 1995; Venkatesh & Bala, 2008; Venkatesh & Davis, 2000).”
BackgroundMeasuring engagement in video game-based environments: Investigation of the User Engagement Scale
Wiebe, E., Lamb, A., Frankosky, M. H., & Sharek, D. J. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2013.12.001 · Cited by 309 · see citing works
“…and usability have a long, established history in research in human–computer interaction (cf., Shneiderman, 1998) and form the backbone of well-established frameworks such as the Technology Acceptance Model (Bourgonjon, Valcke, Soetaert, & Schellens, 2010; Venkatesh & Bala, 2008).”
MethodWells, J. D., Campbell, D. E., Valacich, J. S., & Featherman, M. S. (2010) · Decision Sciences · doi:10.1111/j.1540-5915.2010.00292.x · Cited by 309 · see citing works
“From a reward perspective, factors such as ease of use, trust, and usefulness have been shown to positively influence adoption intentions (Gefen, Karahanna, & Straub, 2003; Venkatesh et al., 2003; Venkatesh & Bala, 2008).”
BackgroundInfluentialAugmented reality smart glasses: an investigation of technology acceptance drivers
Rauschnabel, P. A., & Ro, Y. K. (2016) · International Journal of Technology Marketing · doi:10.1504/ijtmkt.2016.075690 · Cited by 306 · see citing works
“For example, TAM2, TAM3, and UTAUT all incorporate ‘image’ as a factor.”
BackgroundInfluentialCross-cultural differences in adopting mobile augmented reality at cultural heritage tourism sites
Jung, T. H., Lee, H., Chung, N., & Dieck, M. C. T. (2018) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-02-2017-0084 · Cited by 306 · see citing works
“In fact, perceived enjoyment was found to be a strong determinant of ease of use within a variety of studies and contexts (e.g., Venkatesh, 2000; Venkatesh & Bala, 2008).”
Unpacking teachers’ intentions to integrate technology: A meta-analysis
Scherer, R., & Teo, T. (2019) · Educational Research and Reviews · doi:10.1016/j.edurev.2019.03.001 · Cited by 295 · see citing works
Choi, J. R., & Kim, S. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.06.007 · Cited by 302 · see citing works
- Authors
- Venkatesh, V., & Bala, H.
- Affiliations
- University of Arkansas at Fayetteville (US)
- Indiana University Bloomington (US)
- Corresponding author
- Venkatesh, Viswanath
- Bala, Hillol
- Venue
- Decision Sciences
- Publisher
- Wiley
- Published
- 2008-05
- Volume
- 39
- Issue
- 2
- Pages
- 273-315
- ISSN
- 0011-7315 (ISSN-L)
- 1540-5915
- OpenAlex
- W2112042732
- Cited by
- 8,159
- References
- 135
- Citations by year
- 2026: 909 · 2025: 912 · 2024: 780 · 2023: 746 · 2022: 717 · 2021: 645 · 2020: 556 · 2019: 499 · 2018: 396 · 2017: 379 · 2016: 320 · 2015: 324 · 2014: 287 · 2013: 266 · 2012: 186
- FWCI
- 175.14
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Decision Sciences (publishedVersion)
- Zenodo (CERN European Organization for Nuclear Research) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Sociology and Political Science › Information Systems Theories and Implementation
- Social Sciences › Business, Management and Accounting › Management Information Systems › ERP Systems Implementation and Impact
- Keywords
- technology acceptance model, information technology adoption, information technology use, perceived usefulness, perceived ease of use, management interventions, IT implementation, organizational decision-making, nomological network
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