The acceptance of new technologies is a prominent problem in the healthcare arena. The attitude of target users towards innovations plays a pivotal role. End users will decide to use or misuse them, to incorporate them into their routine or work around them . The use of new technologies is expected to steadily increase in healthcare including dentistry. With technological advances comes the challenge of how best to use them in dental education. Educational researchers are challenged to test the effectiveness and efficiencies of these new methods . The ability to identify, predict, and manage acceptance of technology will facilitate implementation efforts. Therefore, the present study aimed at assessing the acceptance of virtual dental implant planning software by undergraduate students.
In the past, different models have been developed to predict and explain the end-user reactions to new technologies . The technology acceptance model and the extended technology acceptance model have been tailored to the information systems context . However, the technology acceptance models have not been developed specifically in or for the healthcare context. If used in their generic form, they may not capture or may contradict some of the unique contextual features of computerized healthcare delivery. Therefore, several additions and modifications have been made to adapt the technology acceptance models to the healthcare arena . Today, they have widespread application in explaining healthcare providers’ reactions to new technologies. The increase in the use of the technology acceptance models appears justified. Many of the relationships specified by technology acceptance models have been repeatedly validated in healthcare settings .
An alternative model in the acceptance of new technology is the theory of planned behavior . Its validity has been demonstrated in the healthcare sector . It is theorized that perceived behavioral control is an additional important determinant of behavioral intention that is missing in the technology acceptance models. Consequently, the technology acceptance model and the theory of planned behavior have been combined in an integrated model, which was successfully used in the healthcare sector . In order to be able to make use of all the different constructs the combined model (C-TAM-TPB) was adopted in the present study.
The reliability of the measurements of the different constructs in the present study compared well to other trials on technology acceptance in different fields of healthcare. Cronbach’s α values ranging from .55 to .93 have been described by different authors [9, 15, 16]. The range of the Cronbach’s α values was not that pronounced in the present study (.71–.85, Table 2). However, the Cronbach’s α values were always above .7, indicating an acceptable reliability .
In the present study C-TAM-TPB did not include demographic data. Significantly more female students were included and the age range was small. Therefore, it was decided not to base the analysis on these aspects. Previous studies on acceptance of technology have already shown the limited relevance of demographic data in the healthcare context .
In the present study perceived usefulness showed a significant correlation to attitude (p = .002) as well as behavioural intention (p = .001, Table 3). As with previous studies perceived usefulness appeared to be one of the most important factors affecting the students’ acceptance of the new technology . On the other hand, perceived ease of use did not show a significant correlation with either perceived usefulness or attitude (Table 3). It seems that dental students are pragmatic in their technology acceptance decisions, appearing to focus on usefulness in technology assessment. They tend to accept a technology when it is considered to be useful to their practice independent of whether the use of the technology is convenient. This finding is consistent with the results of previous studies that showed that usefulness is more important than ease of use .
Such results have especially been found for physicians. It has been hypothesized that physicians have relatively high general competence and mental/cognitive capacity and may comprehend the use of a technology quickly. They seem to become familiar with operations of new technologies without going through the intense training that might be necessary among other user populations . The same might be true for dental students. It seems that dental students will be able to successfully face the future challenges of implementation of new technologies in undergraduate curricula.
At the Dental School of the University of Erlangen-Nuremberg students are confronted with implant dentistry from the first day on. They gather profound theoretical knowledge in the field. Moreover, the students have the possibility of observing implant surgery and treating selected patient cases prosthodontically by themselves. However, so far there was a gap as far as the use of the acquired knowledge on implant dentistry for treatment planning was concerned. The use of virtual implant planning software in an undergraduate setting seems to be a relevant solution for this problem . The students are put into a position where they can plan implant treatment and virtually place and restore these implants. They are enabled to transfer their theoretical knowledge to a more practical situation. It can be assumed that the addition of virtual implant planning to an undergraduate curriculum leads to a deeper understanding of implant dentistry. Providing copies of the software to the students enables them to use the virtual dental implant planning tool at their convenience as long as they want to. Getting feedback from the supervisors on the quality of the planned patient cases leads to an additional increase of the learning effect and supports further reflection on the topic . As a consequence, the good acceptance of the virtual dental implant planning software by the students in the present study does not seem be surprising.
The major limitation of the study is the sample size. It does not allow use of structural equation modelling to analyze causal relations between model parameters. For the estimation of the parameters it is necessary to adopt a maximum likelihood estimation, which requires a sample size of at least 100 .