Augmented reality for virtual training in the construction industry

Abstract

BACKGROUND:

Technology has evolved in surprising ways, and augmented reality (AR) has positioned itself as one of the technologies with outstanding value. Its importance in education is still being debated, but its incorporation in business training has been left out.

OBJECTIVE:

This study aims to determine variables that explain the intention to use this technology in construction training, focused on preventing injuries and disabilities.

METHODS:

This study was carried out using the Technology Acceptance Model (TAM) through the structural equation method. The variables: perceived ease of use, perceived usefulness, attitude toward using, and behavioral intention to use were incorporated. An AR app was developed that addresses the teaching of safety elements on scaffolding, and we collected data from Chilean construction companies.

RESULTS:

The results show that perceived usefulness and perceived ease of use explain the attitude towards using augmented reality, while perceived usefulness and attitude towards using explain behavioral intention to use.

CONCLUSIONS:

These findings enrich AR’s literature in the construction industry and have high business managers’ implications. It may allow them to implement this technology more likely to succeed in their virtual business training to prevent injury-related disability.

Keywords

Business training augmented reality TAM technology acceptance

1 Introduction

Today, many companies must address the challenges posed by non-planned difficulties, such as the COVID-19 pandemic. The various containment, curfew, and closure strategies of industries have presented challenges that companies must address to survive. The use of technology has positioned itself to maintain some activities, primarily meetings via streaming platforms. Nevertheless, there are other resources that companies can use to keep running crucial activities like employee training, such as augmented reality.

During the last decade, augmented reality (AR) technology has reached prominence in several areas of knowledge due to its versatility and diverse application possibilities [1]. Augmented reality can be defined as the direct or indirect vision of the real world’s physical environment, whose elements are combined with virtual elements to create a mixed reality in real-time through technological or computational devices [2, 3].

With the evolution of mobile devices and their increasingly intuitive interaction platform, technologies such as augmented reality have been given new application opportunities, achieving the incorporation of virtual elements in space through mobile devices [4]. Today, augmented reality has applications in various fields such as tourism, medicine, entertainment, military application, aeronautics, and education [5]. It also has supported product development, such as to support high-value, high-knowledge manual work throughout the product life cycle [6] and evaluate its usability [7, 8]. Additionally, it has been used to reduce workers’ workload and stress [9].

Incorporating these technologies in training processes has become relevant [10], considering that the interaction provided by it allows users to recognize situations more intuitively, making skill development a more efficient process [11]. Besides, augmented reality technology enables the integration of theoretical aspects and practical experience to be more consistent since processes such as memory, attention, and concentration are based on mental representations, which, thanks to this technology, become more active [12].

Concerning the effect of augmented reality on learning, previous studies have shown significant differences between groups that used augmented reality and those that did not, concluding that this technology contributes positively to learning outcomes [13 –15]. In the education field, this technology makes it possible to make better use of students’ abilities due to higher interaction with the content in its digital representation, in addition to influence variables such as motivation, confidence, attitude towards learning, and satisfaction [16 –20].

In the context of business training, augmented reality applications focus on training processes. Previous studies have shown favorable results linked to the incorporation of these technologies, impacting on the reduction of task execution time [21, 22], error reduction [23, 24] and decrease in the time needed to teach tasks [25].

Added to this technology’s projected expansion, it offers excellent possibilities for incorporating augmented reality in business training in different business contexts [26, 27]. Studies report using these technologies applied to the construction field in architecture and design visualization, training in construction health and safety, training in equipment and operational tasks, and structural analysis [28].

In Chile, the vast majority of construction safety training uses a traditional approach. The instructor is the center of the training process [29], and there is a call for special attention to health and safety training for users [30]. Being this last one, an urgent matter in the pandemic context that the planet is currently experiencing. Historically, the construction industry has had high accident rates [31], and considerable amounts are invested in training to prevent these rates. In 2018, the OTIC (Intermediate Technical Training Organization) of The Chilean Chamber of Construction declared approximately US$10 million as a direct investment in training [32].

Therefore, this work aims to determine if augmented reality technology could be accepted by trainers and training managers in the construction industry, in the context of business training, to contribute to the development of new training systems that integrate these technologies and improve learning instances.

By determining the acceptance of augmented reality in the context of business training in the construction area, we seek to promote this technology’s use and support these companies’ economic growth. With the above, we expect that the new training processes will reduce the failures and errors in this area’s activities and be a real possibility to continue developing the construction activities, even in the pandemic context.

In construction, the information technologies used can be classified into five groups: Scheduling programs; Design programs; Information technology communications; Wireless technology; y Management programs [33]. One of the most studied about its acceptance is building information modeling (BIM), belonging to the Design programs. Using these modern construction technologies improves quality and productivity and reduces time and costs. [34]. Moreover, while the introduction of BIM is high, BIM user proficiency is low. [35]. Besides, in many projects, BIM application has not achieved the expected benefits due to user resistance. [36]. Along with the above, technological acceptance in the construction field has also been studied in Alternative dispute resolution (ADR) methods [37], Blockchain adoption [38], and technologies in general [39].

On the other hand, the construction industry does not escape from the implementation of Industry 4.0 either. Technological acceptance has been studied in the Off-site construction (OSC) method. With digital technologies associated with the Industry 4.0 concept, OSC can offer a higher productivity and safety rate [40]. It has also been addressed in the trending technology of intelligent contracts (iContracts) [41].

Furthermore, while in construction, the discussion of digitization has gained momentum in recent years and is acclaimed worldwide as drivers of productivity gains, they are not yet used on a large scale [42]. The construction 4.0 industry’s technology adoption processes must also consider different management areas and stakeholders [43].

One of these areas is construction safety management through Construction Safety Risk Mitigation. The construction industry should adopt innovative practices throughout the project life cycle to improve construction safety performance [44]. Thus, the adoption and implementation of innovative solutions effectively improve construction safety [45]. However, more research is needed regarding adopting safety technologies in the construction industry [46].

Therefore, we will study factors that may explain AR technology’s acceptance in the construction field, specifically in training to prevent injury-related disability. For this research, training in the use of scaffolding in construction has been established as the object of study, taking into consideration that statistics corresponding to the Metropolitan Region of Chile present an alarming reality in terms of accident rates associated with the use of scaffolding and falls from heights [47].

2 Theoretical framework

The Technology Acceptance Model (TAM), introduced by Davis in 1989 [48], sought to answer why users use technologies. The model is based on the theory of reasoned action (TRA), which states that individuals’ behaviors depend on their beliefs and subjective norms.

Davis proposes four variables to determine current technology use: perceived ease of use (PEOU), perceived usefulness (PU), attitude towards using (ATU), and behavior of intention to use (BIU). Perceived ease of use is defined as the degree to which a person believes that a specific technology can be used effortlessly [49]. Perceived usefulness can be characterized as how a person thinks a certain technology will improve task performance [50], such as the shorter time necessary to perform a task or activity or higher precision [49]. Attitude toward using refers to the user’s evaluation regarding the convenience of using a determined technology [51]. Finally, behavioral intention to use refers to an individual’s perception of what others think he should do about a determined behavior [52].

Davis noted that technology’s use relies fundamentally on the mediation between perceived ease of use and perceived usefulness. These two variables influence the attitude towards using that, at the same time, determines the behavior of intention to use and this, the current use of technology.

Hu et al. [53] point out that “The intensity of use towards technology can be explained or predicted by the attitude towards using and perceived usefulness of the technology.” Also, user acceptance of a technology system can be examined through intended use behavior rather than the actual use of the system [54, 55] because there is a significant causal relationship between the intention to use and the actual use [56]. That is practical for studies that analyze technologies that are not widely used in the context to be explored [53].

3 Methodology

3.1 Application (App)

An augmented reality application was used that addresses the teaching of safety elements on scaffolding. With the application, it is possible to interactively visualize its security elements in three dimensions, its assembly and use. The application consists of five instances. In the first one, the different fastening points to which the lifelines should be anchored are highlighted by the arrows. Among these are: the inner cartouche, the top box for handrails, the frame rail, the horizontal one, the rosette between braced nodes, and the fully braced rosette. The second instance highlights an element of vital importance to prevent falls, the horizontal railing highlighted with a red color. The problem with this railing is that it is not always assembled, thus creating unnecessary risks. The third instance highlights the skirting boards, another element that seeks to avoid falls and accidents and which, like the previous element, tends not to be assembled. The fourth instance shows the diagonals, which are vital for the scaffolding structure and its stability. Special care must be taken to fix them to avoid accidents and falls. Finally, it shows the plates with the spindle, which are the bases that give support to the structure, and its correct assembly is vital to avoid collapses of the structure (Fig. 1).

Fig. 1

AR application on scaffolding used in the study.

A video accompanied the survey to ensure that respondents understood the concept of augmented reality, its benefits, and how the application works.

3.2 Sample

Companies in the Coquimbo Region’s construction sector were considered subjects of study, specifically those responsible for integrating augmented reality technologies into training and trainers. Under these conditions, this study considers 31 companies, a sample of 29 companies, for a confidence level of 95% and 5% error.

3.3 Theoretical research model

The Technology Acceptance Model (TAM) will be used in its reduced version [56] to measure the acceptance of augmented reality in training processes in the construction industry. Technological acceptance will be defined as “an individual psychological state regarding a voluntary or intentional use of a particular technology” [57].

The fact that an application is easy to use can be a factor for workers and companies to incorporate them into work processes. Studies with AR apps have shown how perceived ease of use has on perceived usefulness [58]. Simultaneously, perceived ease of use has also influenced attitude toward using [58, 59]. The above leads to our following hypotheses:

H1: Perceived ease of use has a positive effect on perceived usefulness.

H2: Perceived ease of use has a positive effect on attitude toward using.

Concerning perceived usefulness, it has also been shown to influence attitude toward using when AR technology has been used [60]. It has also been theorized that perceived usefulness may influence behavioral intention to use [60, 61]. Studies conducted in AR have shown that behavioral intention to use is also influenced by attitude toward using [50, 59]. The above leads to our last hypotheses:

H3: Perceived usefulness has a positive effect on attitude toward using.

H4: Attitude toward using has a positive effect on behavioral intention to use.

H5: Perceived usefulness has a positive effect on behavioral intention to use.

Therefore, a model is proposed that explains that the intention to use technology, in this case, augmented reality, will be explained by the perceived ease of use, perceived usefulness, and attitude towards using, which will be tested using a structural equation model (Fig. 2).

Fig. 2

Model used in research.

In this context, the model hypothesis states that the degree to which augmented reality is perceived as user-friendly by trainers and training managers directly affects their perception of technology’s usefulness and attitude towards it. Attitude is also influenced by the perceived usefulness of augmented reality technology. Finally, the intention to use, which allows examining the acceptability of the system [54] by trainers and training managers, can be explained/predicted based on the attitude towards using augmented reality and the technology’s perceived usefulness.

3.4 Measures

One of the advantages of using the TAM is that this model has been widely studied and validated [48 , 63]. Specifically, the scales for measuring the variables perceived usefulness, perceived ease of use, attitude towards using, and behavioral intention to use had been obtained from previous studies [53 , 64]. The statistical instrument consisted of 21 questions grouped into four constructs (Table 1). The questions answered using a 7-point Likert scale, where one corresponded to “strongly disagree” and seven to “strongly agree.” The scale was tested for reliability and construct validity [65].

Table 1
Variables and number of items

Variable Number of items Source

Perceived usefulness 6 Davis [48]

Perceived ease of use 6 Davis [51]

Attitude towards using 3 Hu et al. [53]

Behavioral intention to use 6 Hu et al. [53]

Variable	Number of items	Source
Perceived usefulness	6	Davis [48]
Perceived ease of use	6	Davis [51]
Attitude towards using	3	Hu et al. [53]
Behavioral intention to use	6	Hu et al. [53]

3.5 Data collection

The data collection process was generated by applying a survey through the “Google Forms” platform. That was due to its ease of use and the benefit of automatic data classification. A pre-test was conducted with three construction experts to ensure that the instrument was appropriate [66]. Subjects in the study were contacted via e-mail and telephone. Subsequently, visits were made to company representatives. The sample number was 31 companies. The application was carried out for two months, obtaining 89% of duly completed questionnaires.

4 Results

The validity of the measurements was evaluated in terms of the reliability and validation of the constructions. Reliability was assessed through the calculation of Cronbach’s Alpha, as shown in Table 2. The values were very close to or above 0.90, based on the literature’s recommended limits [67].

Table 2
Descriptive statistics and Cronbach’s Alpha

Average Standard Deviation Cronbach’s Alpha

Perceived usefulness 0,89

PU1 5,09 2,63

PU2 5,36 2,41

PU3 5,18 2,33

PU4 5,23 2,45

PU5 5,36 2,32

PU6 5,00 3,36

Perceived ease of use 0,93

PEOU1 5,14 2,75

PEOU2 4,59 2,88

PEOU3 4,86 2,03

PEOU4 4,86 3,30

PEOU5 5,18 2,42

PEOU6 5,27 2,20

Attitude towards using 0,89

ATU1 5,50 2,07

ATU2 5,14 2,57

ATU3 5,32 2,31

Behavioral intention to use 0,96

BIU1 5,14 2,30

BIU2 5,14 2,57

BIU3 5,09 2,45

BIU4 4,77 3,27

BIU5 5,05 2,50

BIU6 5,00 2,82

	Average	Standard Deviation	Cronbach’s Alpha
Perceived usefulness			0,89
PU1	5,09	2,63
PU2	5,36	2,41
PU3	5,18	2,33
PU4	5,23	2,45
PU5	5,36	2,32
PU6	5,00	3,36
Perceived ease of use			0,93
PEOU1	5,14	2,75
PEOU2	4,59	2,88
PEOU3	4,86	2,03
PEOU4	4,86	3,30
PEOU5	5,18	2,42
PEOU6	5,27	2,20
Attitude towards using			0,89
ATU1	5,50	2,07
ATU2	5,14	2,57
ATU3	5,32	2,31
Behavioral intention to use			0,96
BIU1	5,14	2,30
BIU2	5,14	2,57
BIU3	5,09	2,45
BIU4	4,77	3,27
BIU5	5,05	2,50
BIU6	5,00	2,82

The validation of the instrument constructs was evaluated by analyzing convergent and discriminant validity using a poly-correlation analysis, considering the Likert scale’s ordinal nature [68], and factorial analysis. The correlation between the same construct variables is slightly higher than the different constructs’ elements, indicating the measurements’ convergence and discriminant validity. Despite this, caution should be exercised when interpreting these results considering the low difference in magnitudes.

The following are the results of the factorial analysis of the main components. Four components were extracted, which coincides with the number of constructions included in the study model, presented in Table 3. Most items tended to measure the same construct, exhibiting higher load factors on a single component rather than multiples. Items do not meet this condition: PEOU1, PEOU6, and ATU1. Despite this, it is possible to validate the measuring instrument to be applied for the study.

Table 3

Results of factorial analysis: extraction of main components

	Components
	1	2	3	4
Perceived usefulness
PU1	0,64	0,09	–0,09	–0,03
PU2	0,59	–0,02	0,06	0,02
PU3	0,63	–0,11	0,03	0,00
PU4	0,66	0,05	–0,10	0,05
PU5	0,65	0,10	–0,13	0,03
PU6	0,35	0,21	0,04	–0,10
Perceived ease of use
PEOU1	–0,01	0,52	–0,16	0,29
PEOU2	–0,05	0,21	–0,07	0,47
PEOU3	0,14	0,15	–0,05	0,35
PEOU4	0,12	–0,30	0,05	0,72
PEOU5	–0,14	0,15	0,21	0,43
PEOU6	–0,12	0,20	0,35	0,20
Attitude towards using
ATU1	0,31	0,07	0,23	0,05
ATU2	0,21	0,41	0,12	–0,14
ATU3	0,30	0,41	0,09	–0,19
Behavioral intention to use
BIU1	0,07	–0,07	0,50	0,12
BIU2	–0,02	0,08	0,55	0,02
BIU3	0,12	0,03	0,52	–0,04
BIU4	–0,09	0,13	0,63	–0,09
BIU5	0,25	–0,04	0,43	–0,03
BIU6	0,03	0,18	0,51	–0,11

The normalized fit index (NFI) and the non-standardized fit index (NNFI), with values of 0.48 and 0.52, do not fully satisfy the recommended values. That indicates that the model can be substantially improved [69]. The comparative fit index (CFI), with a value of 0.58, suggests that only 58% of the data’s covariance can be reproduced by the model below the desired 90% [70]. Finally, the residual mean square error (RMSR), which represents the square root of the discrepancy between the sample and model covariance matrix, with a value of 0.18, suggests a slight lack of model fit, considering the desired value less than 0.1 [71]. The values are shown in Table 4.

Table 4

Model goodness-of-fit analysis

	Recommended Value	Result of the Study
Chi-square/degrees of freedom	≤3,0	2,73
Goodness-of-fit index (GFI)	≥0,9	0,48
Adjusted goodness-of-fit index (AGFI)	≥0,8	0,35
Normalized fit index (NFI)	≥0,9	0,48
Non-normalised adjustment index (NNFI)	≥0,9	0,52
Comparative adjustment index (CAI)	≥0,9	0,58
Residual mean square error (RMSR)	≤1,0	0,18

The model’s ability to explain was examined in terms of R² for each dependent construct. Perceived usefulness and perceived ease of use were able to explain 75% of the observed variations in attitude towards using. The perceived usefulness seems to contribute slightly more than the perceived ease of use to explain the model’s ability. In parallel, perceived usefulness and attitude towards using were able to explain 79% of the observed behavioral intention variations to use augmented reality technology. The results are presented in Fig. 3.

Fig. 3

Model results.

Most of the data supported the causal relationships proposed by the TAM model. Perceived usefulness had a direct positive impact on attitude towards using, with a path coefficient of 0.60. This coefficient can be interpreted as: for each unit that increases perceived usefulness, attitude towards using will increase by 0.60 units. The relationship between perceived usefulness and behavioral intention to use could not be supported. The effect of attitude toward using on behavioral intention to use was positive and direct, with a coefficient of 0.89. Finally, perceived ease of use had positive impacts on both perceived usefulness and attitude towards using, with a coefficient of 0.42 in both cases. The results are presented in Table 5.

Table 5

Causal relationships

			Estimate	Approximate Standard Error	Critical Ratio	P-value
H1: PEOU	→	PU	0.42	0.23	1.93	0.05^*
H2: PEOU	→	ATU	0.42	0.111	2.557	0.01^**
H3: PU	→	ATU	0.60	0.115	3.331	0.00^***
H4: ATU	→	BIU	0.89	0.364	2.916	0.00^***
H5: PU	→	BIU	–0.01	0.183	–0.024	0.98

^***p 0.001; ^**p 0.01; ^*p.05.

5 Discussion

This study provides valuable information by measuring the acceptance of augmented reality technology in the context of construction training focused on injury and disability prevention. Based on the technology acceptance model theory [64], the model supports the validity that company trainers accept augmented reality applications to train their workers. In the following, we will discuss the results of the study.

The measures used in this study indicated a high degree of reliability being used with satisfactory validity results in several experimental studies. However, there are some anomalies. The results suggest a lack of fit of the model within the study context. Some of the goodness-of-fit indicators were poor with the recommended values, indicating that the model could be improved in its specification. Some explanations for this suggest that there is information that the model did not observe, and the fit could be enhanced by incorporating it. Some constructs that could be added to the model are social influence (measured in terms of subjective norms, willingness, and image) and instrumental cognitive processes (measured in terms of job relevance, product quality, and ability to demonstrate results). Those included in the TAM model’s extension called TAM 2 developed by Ventakesh & Davis [72]. The error, known as type II, may exist due to the small sample size. The above is due to the unwillingness of companies in Chile to participate in studies. This situation has been visualized globally since the response rate has decreased worldwide [73].

Despite the above, the model provides interesting results for construction companies and their training processes and opens other industries’ opportunities to use these technologies. Hypotheses 1 and 2 are accepted in the study and indicate a significant positive relationship. They suggest that companies perceive that augmented reality is an easy-to-use technology. That causes companies to see it helpful to use this technology in their training processes and influences companies’ attitudes. Both effects have an equal size (0.42). However, at a different significance level, 0.05 for PEOU to PU and 0.01 PEOU to ATU shows the importance of developing applications that are easy to use.

Hypothesis 3 presents an effect of 0.60 positive and significant at 0.1% that the companies’ perceived usefulness impacts the attitude towards using augmented reality as a training tool. The R² value equal to 75% explains the variations observed in the attitude towards using, values similar to those reported in Taylor & Todd [74] (73%) and Mathieson [54] (73%).

The intention to use, which is only explained by the attitude towards using (H4), has a positive and significant effect of 0.89. H5 has been discarded, so perceived usefulness has no direct influence on behavioral intention to use. Behavioral intention to use has an R² value equivalent to 79% of the observed variations, a higher value than that reported in other studies Szajna [55] (52%), Taylor & Todd [74] (52%), and Mathieson [54] (70%). That can be explained by the fact that the problem under study has a high impact on workers’ lives. Therefore, alternatives that impact workers’ safety can be considered even though it’s perceived if they are as easy to use or not.

The implications of the findings can be discussed from different perspectives. First, theoretically, a large part of the variance for user behavior is explained by the model. Even though the influence of PU on BIU is not significant, all the remaining relationships are significant and positive. Therefore, it is possible to explain the intention to use augmented reality technology for business training from the ease of use that the application should have, its usefulness, which improves the attitude to venture into technological innovation in companies.

Secondly, the model is a unique exploratory empirical study that had not been addressed before. That provides opportunities to study the use of technology in business training beyond augmented reality. The actual variables included in the model show that TAM is an interesting tool to study technology adoption behavior. Finally, more experiments are needed to test other variables that can explain the behavior of training managers, employers, and workers to use this and other technologies.

6 Conclusions

Today’s advancement and prominence of augmented reality have increased this technology’s possibilities in the construction industry. The above may generate a particular interest in examining or validating this technology’s acceptance within this professional context. The present study examined the applicability of the technology acceptance model in the context of augmented reality as a tool to support training processes within companies in the construction industry to assess its potential acceptance. Data collected from 29 construction companies belonging to the Chilean Chamber of Construction were used to examine the model’s validity.

The contribution of the study to the investigation of the acceptability of technologies should be considered. Although the model fit has limitations, the fit indicators’ values indicate the TAM model’s improvement possibilities. The scales used to measure the TAM methodology were validated. The model’s high explanatory power was also observed, suggesting that it can explain this technology’s acceptance in this context through the current model.

From a business management perspective, the findings indicate that, in order to foster acceptance of augmented reality and intention to use it by potential users, it is critical to promote and cultivate a positive attitude towards the use of this technology. It is worth noting that managing this attitude has a more significant impact than presenting this technology as applicable. Therefore, together with the model’s explanatory ability, a high acceptance rate of augmented reality application could be suggested if the attitude towards using is focused on management. Therefore, processes that influence an organization’s attitude, such as organizational change management and culture, play a leading role in introducing augmented reality technology. Also, the perceived ease of use of the technology plays an important role. That must be reflected in the design and programming of enterprise training applications. The research results provide valuable insights into the most important factors influencing AR as an enabling tool for managers. This question is important considering the advent of Industry 4.0, which will be of such importance and relevance that it will significantly change supply chains, business models, and business processes. Therefore, balancing their work with the use of technology could bring commercial, economic and sustainable impacts.

From the results of this study, it is evident that the literature in the field of acceptance and use of augmented reality requires further research that would be based on acceptance theories other than TAM. An extended version of the existing methodology (such as TAM2), TRAM (technology readiness and acceptance model), or TRI (Technology Readiness Index) could be applied.

The study is not without limitations. First, both the study population and sample are relatively small compared to similar studies, which may be a source of bias when analyzing the TAM methodology’s application within the study context. This situation creates possibilities for future research that can examine the construction industry in terms of a larger population and sample to avoid any bias that may have been generated in this work. The non-significant relationship between perceived usefulness and behavioral use intention needs to be re-examined. That contradicts what was proposed by the original TAM model to establish whether there is a trend within the construction industry or simply an isolated case attributed to local cultural factors. Another limitation is related to the fact that the data were collected from the region. This limitation implies that the generalization of the findings documented here to other environments should be cautioned and requires further research. Future studies covering other regions would be of interest to find out how the results differ.

Footnotes

Acknowledgments

This work was supported by the Unidad de Mejoramiento Docente and Research Department of the University of La Serena (PR18362). We thank Ing. Ricardo Campos-Villarroel for his support in the development of the AR application.

Conflict of interest

None to report.

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Augmented reality for virtual training in the construction industry

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1 Introduction

2 Theoretical framework

3 Methodology

3.1 Application (App)

3.3 Theoretical research model

Table 1 Variables and number of items Variable Number of items Source Perceived usefulness 6 Davis [48] Perceived ease of use 6 Davis [51] Attitude towards using 3 Hu et al. [53] Behavioral intention to use 6 Hu et al. [53]

4 Results

6 Conclusions

Footnotes

Acknowledgments

Conflict of interest

References

Table 1
Variables and number of items

Variable Number of items Source

Perceived usefulness 6 Davis [48]

Perceived ease of use 6 Davis [51]

Attitude towards using 3 Hu et al. [53]

Behavioral intention to use 6 Hu et al. [53]