Acceptability of fully guided virtual implant planning software among dental undergraduate students

Acceptance of new technology is a challenge in the health care sector [18]. Previous studies, on the impact of new technologies in healthcare, reveal that the fit between the technology and the clinical work significantly affects how the target users will reject or accept and incorporate it into their daily practice or work around it [18].

Dental implants have progressively become a crucial part of reconstructive dentistry [15]. In many applications implants provide a viable choice for replacement of missing teeth [15]. Dental students must therefore equip themselves with relevant knowledge about dental implantology technology and be informed of situations where implants may be appropriate during the different phases of treatment planning and execution [19].

Fully guided virtual implant planning software permits users to simulate the location of an implant in 2D and 3D models, identify the inferior alveolar dental canal, trace panoramic views, and calculate bone density [15, 20]. A viable example of a fully guided implant planning software that has been applied for teaching and learning at the undergraduate level combines an immersive head-mounted display, a small hand-tracking device, and a smartphone. The devices are connected to a laptop to provide the user interface. The user’s dominant hand is used to manipulate the 3D dental models while the remaining hand holds the controller (smartphone) to ensure accurate positioning and inclination during implant input [20].

The adoption of novel technologies is expected to rise in dentistry and with such advances comes the problem of how best to implement them in dental education. The greatest challenge still lies with the capacity to recognize, envisage, and control the acceptance of a novel technology because this will affect its implementation [21]. Several technology acceptance models have been developed [22, 23]. A classic example of a technology acceptance model that is of particular interest in the health sector is the theory of planned behavior (TPB) [24]. It is a modified theory of reasoned action that extends beyond the theory of reasoned action to incorporate the idea of perceived behavioral control [25, 26]. It takes into consideration perceived and actual management of the behavior under consideration [26]. Perceived behavioral control affects attitude and intention and can also directly influence behavior [26]. Attitudes towards a certain behavior are a revelation of an individual’s assessment of performing a given behavior. Subjective norm shows an individual’s perception of societal expectations to assume a certain behavior [26]. Perceived behavioral control shows the ease or complexity with which performing the behavior is likely to be. It mirrors both internal and external factors such as the availability of time and resources. Arjen’s Theory of Planned Behavior is commonly used because it performs well across behavioral categories regarding explaining intentions [27] (Fig. 5).

Technology Acceptance Model (TAM) also originates from the theory of reasoned action [28, 29]. TAM has two principal factors affecting an individual’s intention to adopt a novel technology: Perceived Ease of Use and Perceived Usefulness [28, 29]. For instance, a person who believes in the perception that digital games as too challenging to play would be unlikely to use this technology while one who perceives digital games as interesting and easy to learn would be more likely to learn how to use the game [25]. Integrating TAM and TPB models provides greater flexibility especially when investigating factors influencing intention [24]. Due to the technical combinations of various constructs used in this study, the combined model of technology acceptance model and theory of planned behavior (C-TAM-TPB) was adopted.

Internal consistency of measurements of various constructs compared well with similar trials in the healthcare sector. Cronbach’s alpha surpassed 0.7 for Perceived Usefulness, Perceived Ease of Use, Perceived Behavioral Control, and Subjective Norm representing an acceptable internal consistency [9]. However, Attitude and Behavioural Intention reported Cronbach’s alpha values less than 0.7. The low values of Cronbach’s alpha could be due to the low number of questions in the constructs. For instance, Behavioral Intention only had 2 sets of questions (the least) followed by Attitude which had 3 sets of questions. This did not compare fairly with other constructs such as perceived ease of use which had up to 6 sets of questions. The results of our study were consistent with findings of the study by Nkenke et al. using the basic implant planning software [13]. In this study, statistical data such as age, gender or year of study had limited relevance and hence were not included in the statistical evaluation. Spearman’s correlations were significant for all the constructs, especially for useability (Perceived ease of use). Similar findings were reported in the study by Nkenke et al. among German dental students. It can therefore be inferred that that the perceived ease of use and acceptance of VIPS among dental students is not restricted to specific geographic boundaries.

Furthermore, several undergraduate and postgraduate programs incorporate virtual learning technologies in teaching primarily because there are no adverse clinical consequences associated with [30]. Additionally, virtual technologies permit multiple repetitions without additional cost of materials which are essential for learning and the practice of new concepts [31].

The present study has some limitations. There are not many studies on the acceptability of newer dental technologies, especially VIPS. This makes it difficult to compare the findings of our study with similar research papers. Secondly, the present study was conducted on a relatively small sample size of students which makes it hard to generalize the findings of the study.