Assessing Consumers’ Acceptance of AR Wayfinding for Indoor

Shopping Navigation in Singapore

AHMAD SAID1, ZUN ER ANG2, YULITA HANUM P. ISKANDAR2

1International Business Management Program, Management Department,

Bina Nusantara University,

Jakarta,

INDONESIA

2Graduate School of Business,

Universiti Sains Malaysia,

Penang,

MALAYSIA

Abstract: - This study investigates consumers’ acceptance of Augmented Reality (AR) Wayfinding for indoor

shopping navigation toward consumer behavioral intention. This study suggested a conceptual model

investigating major determinants of users’ behavioral intention through the UTAUT model. In this study, 175

respondents were selected using a purposive sampling technique, and a survey method distributed via Google

form was used to collect data, then analyze the collected data from the respondents via SmartPLS (Partial Least

Squares Structural Equation Model. The results revealed that two determinants have a positive and significant

relationship with behavioral intention to use the indoor AR wayfinding system application; they are facilitating

conditions and performance expectancy. Furthermore, Effort expectancy (EE), social expectancy (SE), and

privacy risk (PR) were found to have an insignificant relationship with the behavioral intention of adopting an

AR wayfinding system. Software development in Singapore has reliable, secure technologies and policies that

protect personal information, which would lower consumers’ perceived privacy risks.

Key-Words: - Augmented reality; Wayfinding; Indoor Navigation; shopping navigation, Unified Theory of

Acceptance and Use of Technology, Technology

Received: May 12, 2022. Revised: August 16, 2023. Accepted: September 17, 2023. Available online: October 19, 2023.

1 Introduction

Navigation in indoor commercial buildings can be

difficult due to the complexity of physical

environments, lack of visual access to particular

landmarks, incongruent floor layouts,

incomprehensible signage, and disorienting

staircases. Indoor augmented reality (AR)

wayfinding application systems can overlay the

directional signs onto the real-world setting captured

from the mobile camera sensors and recommend the

most time-saving and efficient route options. The

accuracy of the application allows users to avoid

routes with crowds such as in the airport so the user

can reach the specific gate smoothly or to the

specific bed or department in a hospital. Wayfinding

navigation system-related studies on different

locations have been conducted their research in the

Czech Republic at Ostrava, [1], meanwhile in

Austria, [2], and in Iran at Tabriz, [3].

Indoor AR technology is not only able to help

the user find their direction, destination, and

WSEAS TRANSACTIONS on COMPUTER RESEARCH

DOI: 10.37394/232018.2024.12.4

Ahmad Said, Zun Er Ang, Yulita Hanum P. Iskandar

E-ISSN: 2415-1521

Volume 12, 2024

location with less time but also with less cognition

load. The majority of consumer-based research on

AR in retail concludes that most of the consumer

has a positive response towards retail with AR

technology, [4]. The shopping experience has

significantly enhanced with the AR technology,

many promoting relevant such as marketing

outcomes help to reduce uncertainty from

consumer’s decision, [5], [6], [7], and boosting the

inclination to purchase, [8], [9], develop stronger

customer loyalty to the services and products, [10],

and facilitate consumer to perceive the brand value

and positive relationships, [11], [12]. AR can

improve the consumer’s shopping experience by

giving them a hedonic and utilitarian value, and also

further enhance their decision-making process, and

lead to the ultimate positive behavioral intentions,

[5], [13], [14], on any stages of the consumer

shopping journey throughout the day, [15], [16].

Nonetheless, consumer adoption of AR technology

in retail is slow. This is due to the fact that most

businesses from big corporation groups to small

retailers are still cautious and observing the market,

[17], [18]. With current findings, technical

limitations and privacy risk concerns might hurt AR

wayfinding on the shopping experience, [8], [10],

[13], [19].

From this context, this study seeks to clarify the

direct effect between the independent variables and

the user's behavioral intention to use, aiming to

assess the purpose of adopting this service to face

the wave of AR technology in indoor wayfinding.

This study is different from other authors as the

UTAUT base theory will be used as a starting point.

To meet the technological challenges and structure

the market, assessing the intention of the consumers

in adopting new technology is crucial. The lack of

research about behavioral intention to use indoor

AR wayfinding system applications is

understandable. The wave of AR technology only

started in recent years, and the idea of wayfinding is

still fairly niche. Thus, researchers have an

opportunity to study this phenomenon. Hence, to

explain and fill in the gaps in the existing findings,

UTAUT theory and privacy risk variable are used to

evaluate the relationship between the user

behavioral intentions to use the indoor AR

wayfinding system application.

2 Literature Review

According to the Singapore Tourism Board, [20], AR

Wayfinding will debut as a personal guide to direct

visitors to specific destinations while also providing

relevant information and customized virtual ad

billboards directly to their mobile devices. This might

make navigating, purchasing, or playing gaming

more enjoyable for customers. The upgrade generates

a good and effective style of consumer contact by

meeting the instant needs of users innovatively. As

most of the current studies have concentrated on AR

only, particularly in wayfinding, there isn't much

information that has been released about factors

affecting consumers' behavioral intention to use

indoor AR wayfinding applications.

A study identified the factors that influence

wayfinding in complex environments and developed

an AR-based wayfinding system based on user

experience and requirements, [21]. AR-based

navigation systems have been found to enhance

human indoor cognitive map development and

wayfinding performance, [22]. A navigation and AR

system has been developed for visually impaired

people, which includes a localization system based

on ARKit and a machine learning identification

mechanism, [23]. An adaptive wayfinding

information system based on real-time cognitive load

measures has been found to be effective for

emergency indoor wayfinding, [24]. The correlation

between spatial ability skills and wayfinding

performance using AR-based wayfinding systems

has been explored, [21]. AR-based wayfinding

systems can be effective in reducing navigation time

in complex environments and enhancing wayfinding

performance. Additionally, AR-based systems have

been developed for visually impaired people and

emergency indoor wayfinding.

With reference to the top (20) twenty free

application traffic and GPS navigation apps in the

WSEAS TRANSACTIONS on COMPUTER RESEARCH

DOI: 10.37394/232018.2024.12.4

Ahmad Said, Zun Er Ang, Yulita Hanum P. Iskandar

E-ISSN: 2415-1521

Volume 12, 2024

year 2022, Google Maps is the Top 1 among the rest

of the applications. The strength of Google Maps is

its ability to work both online and offline. Google

Maps allows users to check their route with options

from a location to a destination, featuring real-time

traffic conditions such as accidents, roadblocks,

floods, and other interruptions you might face during

the journey or giving an alternative option to reroute

your journey. Just recently, Google Maps' Live View

feature is now available in Singapore, and it can

auto-update from most of the iOS and Android smart

mobile devices platforms. You can get directions in

the actual world and on a tiny map at the bottom of

your screen using Live View. During the walking

portion of any trip, you can use Live View navigation.

Enter a destination in the search field or tap it on the

map. To assist Maps in finding your location, follow

the on-screen instructions. Therefore, this is a good

time to examine the variables that may gain insights

into the importance of AR wayfinding usage in

consumers' indoor shopping navigation experience.

2.1 Research Hypothesis

There is a wide range of theoretical models that have

been developed to evaluate consumers' usage

intentions with regard to new technology and the

actual use of new technology. An example is Davis'

Technology Acceptance Model (TAM), which is a

well-known model introduced in 1989. The TAM

model has been validated and is often used in mobile,

wearable health-care-related technology, [25], [26],

[27], [28], and also in Management Information

Systems, [29]. However, the TAM alone is still

unable to determine the reception of new

advancements on the grounds, and certain crucial

factors such as social effect in genuine circumstances

are left out of the model, [30].

Then, in 2003 Venkatesh theorized an enhanced

and more comprehensive model for this called the

Unified Theory of Acceptance and Use of

Technology (UTAUT). This is a new IT acceptance

theory consisting of 4 independent variables that will

influence a user’s behavioral intention to use

technology: performance expectancy (PE), effort

expectancy (EE), social influence (SI), and

facilitating expectancy (FE). In this research, the

aforementioned factors will help to examine the

consumer’s intention on AR wayfinding. As a

starting point, performance expectancy refers to the

degree of perception of the technology's usefulness

for improving the performance of usage. Next, an

effort expectation is a measure of the ease with which

a technology can be used. Third, social influence

refers to the degree to which the individual believes

that significant others are expecting him or her to use

a new technology. A facilitating condition, on the

other hand, can be defined as the probability that an

organization and technical infrastructure exist to

provide the capability to use technology. UTAUT has

been introduced to explain information technology

adoption and usage and also has been applied to

examine the consumer’s intention to use Global

Navigation Satellite System, [2], Location Based

System, [29], and Medical Wearable Devices, [30].

Nevertheless, the key concern in the study is that

UTAUT may be unable to completely account for the

consumer’s behavioral intention.

The study, [31], justified that there is a trend

and also increased the use of variables and external

theories in the studies to explain the adoption and

use of technology alongside the UTAUT framework.

A few studies utilized all constructs but without

considering the moderating factors and many others

merely partially utilized some constructs, while

others applied all constructs but without considering

the moderating factors. Moreover, a majority of the

articles that cited the model did so to support an

argument and not to use it effectively. Moderators in

UTAUT were frequently dropped from most of the

studies from, [32] because most of the previous

researchers found that there may not be any

variations in the moderator for the use of new

technologies. However, [32], suggested that future

researchers can also include perceived risk along the

UTAUT model as an additional variable to examine

the direct effect of new technology adoption which

is aligned with the comprehensive literature review,

[33].

When it comes to mobile technology such as AR

or LBS, privacy remains a key concern to consumers.

WSEAS TRANSACTIONS on COMPUTER RESEARCH

DOI: 10.37394/232018.2024.12.4

Ahmad Said, Zun Er Ang, Yulita Hanum P. Iskandar

E-ISSN: 2415-1521

Volume 12, 2024

The study, [34], expressed that because of the misuse

of location information, location-based services

technology has increased the possibility of privacy

violations. In an AR technology and application

context, the perceived and real violations of privacy

concerns remain strong from consumers, [35]. Hence,

integrating the UTAUT theoretical framework with

Privacy Risk would offer a more comprehensive view

of consumer behavioral intention to use indoor AR

wayfinding systems. Therefore, this study will follow

UTAUT as the key theoretical foundation in the

formation of a theoretical model that examines the

behavioral intention to use the indoor AR wayfinding

system.

2.2 Facilitating Conditions

The term facilitating conditions is used to describe an

individual’s belief in the technical infrastructure to

use technology. It reflects the perceptions of external

constraints on behavior created by resource and

technology-facilitating conditions, [36]. Support staff

and guidance availability were highlighted as

assisting users in overcoming technology issues, [37].

The facilitating conditions for this finding

constructed and focused on a technological

environment that is designed to reduce the obstacles

and allow users to use the AR wayfinding system

application capabilities and features more easily. Also,

the AR wayfinding system in the smart mobile device

may be considered an assistant in the shopping mall.

This would benefit from facilitating conditions that

are linked to user behavioral intent. Therefore, the

following proposition can be formulated:

H1: Facilitating conditions positively influence the

behavioral intention of adopting an AR wayfinding

system.

2.3 Performance Expectancy

Performance Expectancy ranks as the most

significant factor to influence consumers’ behavioral

intention in using AR shopping applications, [38]. In

the absence of an AR wayfinding application in

Singapore, the results will not be manifested. The

empirical findings of this study reveal that user

performance expectations are the most important

element in deciding whether or not to use AR

Wayfinding technology from other AR-based

applications such as mobile games and shopping

apps. Therefore, the following proposition can be

formulated:

H2: Performance expectancy positively influences

the behavioral intention of adopting an AR

wayfinding system.

2.4 Effort Expectancy

Another strong predictor in the UTAUT model is

effort expectancy to analyze user behavioral intention

for new technology adoption. As per findings from,

[39], using new technology tends to increase

individuals' effort expectations, and they believed

that the easier the individuals believed in the

smartwatch usage, their intention to use this new

technology device would get as well. A study from

[30], also concludes that if one’s effort expectancy for

smartwatches is greater, the individual’s behavioral

intention to use the smart watches as their own fitness

and health monitoring device will increase. As effort

expectancy is directly related to the fact that the user

is using an AR-based application, [38]; minimizing

the cognitive overload is a key factor in the AR

wayfinding system. Therefore, the following

proposition can be formulated:

H3: Effort expectancy positively influences the

behavioral intention of adopting an AR wayfinding

system.

2.5 Social Influence

The layman's term for social influence means the user

perceives importance from other people such as

family, friends, and artists. In recent years,

location-based social network platforms have been

gaining popularity. A positive relationship between

social influence factors in the prediction of

behavioral intention, [39]. The qualitative interviews

from, [40], revealed that location-based social

networks not only tend to change users’ mobility

patterns but also able to influence how other users

experience technology. As a result, individuals are

more likely to decide to use new technologies after

considering other people's opinions and the following

WSEAS TRANSACTIONS on COMPUTER RESEARCH

DOI: 10.37394/232018.2024.12.4

Ahmad Said, Zun Er Ang, Yulita Hanum P. Iskandar

E-ISSN: 2415-1521

Volume 12, 2024

proposition can be formulated:

H4: Social influence positively influences the

behavioral intention of adopting an AR wayfinding

system.

2.6 Privacy Risk

In this context, privacy risk is the degree to which

the individual believes he or she has control over the

gathering and use of their personal information, even

after it has been exposed. There have been studies

that looked at the effects of privacy risks on

behavioral intentions in the context of location-based

services, but only a small amount of research

especially in the leisure and shopping malls context

have been conducted on the effectiveness usage of

the augmented reality and also the privacy concerns.

One of the AR Google Glass research from, [41],

points out that society is already moving in the

direction of data sharing, and it eventually must

adopt a new privacy policy stating, ‘I think that is

where we are going anyway, and people will get

used to it. In the e-commerce context, privacy

concerns have a negative impact on behavioral

intention, [42]. As AR wayfinding systems are also

part of the location-based services (LBS), [29]. Due

to the need to disclose a user's location information

to use LBS, [29], believes that LBS may pose a

significant risk of privacy infringement. Having

identified four categories of privacy, [43],

categorizes them as location privacy, electronic

communication privacy, individual Info information

privacy, and public place privacy. As people use their

devices in public, there is a risk of being filmed or

recorded in public by random people and it has

become an extremely inevitable issue. Therefore, the

following proposition can be formulated:

H5: Privacy risk negatively influences the

behavioral intention of adopting an AR wayfinding

system.

3 Research Method

3.1 Research Design/Sampling Procedure

The current study aims to explore the user behavioral

intention to use AR wayfinding applications. The

literature review was used to find the existing gap in

the literature, to explore and define the variables, and

to develop the hypothesis. The theoretical research

framework considers facilitating conditions,

performance expectancy, effort expectancy, social

influence, and privacy risk as the independent

variables (IVs), and behavioral intention as the

dependent variable. A quantitative approach is

adopted for this research. To determine the sample

size for this study, the G*Power software was utilized

and it indicated that the minimum sample size for the

current study is 138 (effect size: 0.15; power: 95;

number of predictors: 5). Google forms were

distributed to the target respondents via WhatsApp

and Facebook. The data collection for this study was

carried out from 2nd February 2022 until 2nd May

2022. The study successfully gathered a total of 175

valid responses from shoppers in Singapore.

3.2 Measurement

All of the measurement items used in this study were

adapted from the previous related studies to ensure

the validity of the constructs. Construct validity is a

critical measurement concept in research

methodology that assesses the quality of how the

theoretical construct is measured. It demonstrates

that the research method or test measures the concept

it claims to measure. To ensure the validity of a

construct, researchers use several measurement

items. Researchers articulate a set of theoretical

concepts, develop ways to measure the constructs

proposed by the original theory, and test the theory

empirically, [44]. It is important to recognize and

counter threats to construct validity for a robust

research design. The most common threats are poor

operationalization, experimenter expectancies, and

subject bias, [45]. Poor operationalization refers to

the failure to define the construct clearly and to

measure it accurately. Experimenter expectancies

WSEAS TRANSACTIONS on COMPUTER RESEARCH

DOI: 10.37394/232018.2024.12.4

Ahmad Said, Zun Er Ang, Yulita Hanum P. Iskandar

E-ISSN: 2415-1521

Volume 12, 2024

refer to the researcher's expectations influencing the

results. Subject bias refers to the participants'

expectations influencing the results, [45].

Section 1 collects demographic data of the

respondents and helps to ensure the respondents are

shoppers in Singapore who are aged 18 years and

above and have not experienced augmented reality

wayfinding applications. Section 2 is related to the

variables respectively. Privacy risk was adapted

from, [46], and the questionnaires incorporated a

five-point Likert scale ranging from one to five,

where 1 = Strongly Disagree, 2 = Disagree, 3 =

Neutral, 4 = Agree, and 5 = Strongly Agree.

Meanwhile, variables for Performance Expectancy,

Effort Expectancy, and Social Influence were

adapted from [47], Facilitating Conditions, [39], and

Behavioral Intention, [48], incorporating a

seven-point Likert scale ranging from one to seven,

where 1 = Strongly Disagree, 2 = Disagree, 3 =

Slightly Disagree, 4 = Neutral, 5 = Slightly Agree, 6

= Agree, and 7 = Strongly Agree. The items used to

measure the variables are presented in Table 1

(Appendix). After completing the data collection

process, SPSS and SMART-PLS were used to

analyze the data collected. The partial least

squares-structural equation modeling (PLS-SEM)

approach was chosen for this research.

4 Research Findings

4.1 Demographic Profile of Respondents

This study adopted a qualitative approach.

Purposive sampling was used to filter and ensure the

respondents live in Singapore, are 18 years old and

above, and have not experienced augmented reality

(AR) navigation applications previously. There were

in total 175 respondents who responded to the

research survey that was distributed via Google

Forms. Table 3 summarizes the demographic profile

of the respondents. This research received 68% of

responses from males and 32% of responses from

females, 49.2% had at least a bachelor’s degree or

higher, meanwhile, 38.3% of the respondents had a

diploma and only 12.6% had a secondary school

qualification. More than half of the respondents

used non-AR navigation applications at least once a

day (70.9%), and only 17.1% of

The respondents used the non-AR navigation

application multiple times weekly, and 12% of

respondents used the non-AR navigation application

less than once weekly. In terms of smart devices,

50.3% of the respondents used Android, 41.7% were

iOS device users and 8% used a Microsoft device.

Regarding confidence in mobile applications, almost

all of the respondents (98.9%) have confidence in

the mobile applications they use, and (1.1%) of the

respondents are not confident about the mobile

applications that they use. Table 2 (Appendix)

summarizes the demographic profile of the

respondents in Singapore.

4.2 Measurement Model

Discriminant validity and convergent validity are

the measurement models that were assessed in the

study. HTMT is a method to evaluate discriminant

validity, which is one of the most significant

components of model evaluation, [49]. If the HTMT

value is below 0.85, it demonstrates that

discriminant validity has been established between

two reflective constructs. The result of the HTMT

ratio for data tabulated in Table 3 shows that all the

upper threshold values are less than 0.85. Therefore,

discriminant validity was ascertained. The HTMT

ratio values shown in Table 3 range from 0.261 to

0.779. Furthermore, the highest HTMT ratio has a

value of 0.779, which comes from PerEx and Behan,

and the lowest HTMT ratio is owned by PriRis and

PerEx, with a value of 0.261.

Factor loadings, average variance extracted

(AVE), and composite reliability (CR) were used to

determine if the measurement model had convergent

validity. In the following Table 4, we can find that

the factor loadings were all greater than 0.7, which

were undertaken, [50]. Next, Composite Reliability

above 0.7 or above was considered satisfactory, and

AVE obtained 0.5 or higher is acceptable, [50]. As a

consequence, all the convergent validity criteria

were met in this finding. In general, the Loading

WSEAS TRANSACTIONS on COMPUTER RESEARCH

DOI: 10.37394/232018.2024.12.4

Ahmad Said, Zun Er Ang, Yulita Hanum P. Iskandar

E-ISSN: 2415-1521

Volume 12, 2024

value of convergent validity ranges from 0.7 to 0.9,

with the highest Loading value being in the PriRisk

variable. Then, the lowest value is in the SocInf

variable. Cronbach's Alpha, rho_A, and Composite

Reliability (CR) values range from 0.9 to 0.9, with

the highest value being the BehInt variable and the

lowest value being the PerExp variable. Then, the

Average Variance Extracted (AVE) value has a

lower value range than the others, namely 0.7 to 0.8,

with the highest value being in the BehInt variable

0.841 and the lowest being 0.728 in the PerExp

variable.

Table 3. Discriminant Validity Using HTMT Ratio

BehInt EffExp FacCon PerExp PriRisk SocInf

BehInt

EffExp 0.689

FacCon 0.699 0.610

PerExp 0.779 0.771 0.561

PriRisk 0.299 0.386 0.449 0.261

SocInf 0.614 0.614 0.766 0.573 0.365

4.3 Common Method Bias

SPSS 22.0 common method bias with Harman’s

single factor has been used in the study to test all the

questionnaire findings to ensure that there is no

common method bias in the findings. Typically the

measurement bias in the questionnaire is due to not

measuring the construct directly, but rather the

measurement method. The threshold level of 50%

and based on the finding there is only 45.345% (less

than 50%) of variance for the first factor as shown

in Table 5, [51]. In general, the variance value varies

from 0.076% to 45.345%, with a total component of

20. In addition, the Cumulative value also has a

value range from 45% to 100%. The variance value

is the opposite of the cumulative value. In other

words, the greater the variance value, the smaller the

cumulative value. However, this does not apply to

the variation and cumulative values of component 1.

For instance, the variation value of component 2 is

13.79, which is a high value and has a small

cumulative value of 59.14%.

4.4 UTAUT Structural Model

Fig. 1: Structural Model

Bootstrapping procedures were tested with a

resample of 5,000 to assess all the relationships

between the structural model (Figure 1), and its

corresponding beta (β) and T values, [52]. As seen

in the results in Table 3 (Appendix), facilitating

conditions (β = 0.331, t = 0.092, p = 0.000) and

performance expectancy (β = 0.383, t = 0.097, p

=0.000) were found to have a positive and

significant relationship with intention to use indoor

AR wayfinding system application. However, effort

expectancy, privacy risk, and social influence

showed no significant relationship to the use of

indoor AR wayfinding system applications.

Studies have shown that AR-based wayfinding

systems can significantly reduce the time required for

navigation in complex environments,

[21]. Additionally, research has explored the

correlation between spatial ability skills and

wayfinding performance using AR-based

wayfinding systems, [48], [49], [53]. Other studies

have investigated the impact of navigation aids on

wayfinding performance and perceived workload in

indoor-outdoor campus navigation, [54]. Overall,

while the intention to use indoor AR wayfinding

system applications may not be influenced by certain

variables, the use of AR-based wayfinding systems is

effective in reducing navigation time in complex

environments. This gives support for Hypothesis 1

and Hypothesis 2, whereas Hypothesis 3,

WSEAS TRANSACTIONS on COMPUTER RESEARCH

DOI: 10.37394/232018.2024.12.4

Ahmad Said, Zun Er Ang, Yulita Hanum P. Iskandar

E-ISSN: 2415-1521

Volume 12, 2024

Hypothesis 4, and Hypothesis 5 were rejected (Table

7, Appendix).

5 Discussion

Facilitating conditions (FC) show a positive and

significant relationship between behavioral

intentions to use the indoor AR shopping wayfinding

application. This positive result gives an explanation

that the indoor AR wayfinding system results from

not only the degree of technical resources or

knowledge but also whether the consumer tries to

solve the problem on his or her own or relies on

others should be considered in solving technical

problems.

On the other hand, performance expectancy (PE)

likewise has an incredible effect impact the

consumer’s behavioral intention to use the indoor AR

shopping application which is also aligned with the

finding of [38]. This implies that promoting the

function and convenience of indoor AR wayfinding

systems should be paramount. Therefore, retailers

and operators should develop strategies to promote

the benefits of AR wayfinding not only to individual

consumers but also to other stakeholders who may

affect them.

Nonetheless, effort expectancy (EE) was viewed

not as fundamentally related to behavioral intention

to adopt an indoor AR wayfinding system application,

which contrasted with the finding of [30], and the

outcome could be additionally explained that the

majority of the respondents are tech-savvy and have

an elevated degree of information on media

innovation. According to the data collected, they

perceived that the AR wayfinding system application

was simple and didn't require a lot of exertion.

Furthermore, social expectancy (SE) was found

to have an insignificant relationship with the

behavioral intention of adopting an AR wayfinding

system. This means that respondents’ family and

friends’ opinions will not influence them to adopt an

indoor AR wayfinding system application.

Individuals change their attitudes to increase opinion

differences to negatively evaluate others, [55].

The added variable of privacy risk (PR) was

also found to have an insignificant relationship with

the behavioral intention of adopting an AR

wayfinding system. The studies of privacy risk on

users for new technology acceptance have limitations

in integrating various types of risks into a single

concept and measuring their influence. In this respect,

this study contributes to a more complete

understanding of Singaporean consumers on AR

wayfinding navigation usage by extending the

UTAUT model by focusing on privacy risk. Software

development in Singapore has reliable, secure

technologies and policies that protect personal

information which would lower consumers’

perceived privacy risks. Moreover, the benefit from

that is Singapore has a robust regulatory framework

for intellectual property (IP) protection, [56]. This

can help protect consumers' personal information

from being misused or stolen by unauthorized

parties. Singapore has a Model Framework for AI

Governance that guides to helps organizations

navigate the complex ethical questions that often

arise when AI technologies and solutions are

deployed, [57]. This can help ensure that consumers'

personal information is used ethically and

responsibly.

Finally, two of the Hypothesis statements are

accepted. Those can be associated with AR-based

wayfinding systems can provide clear and legible

environmental information, and facilitate the

cognitive process of route strategy by overlaying the

routing. This can make it easier for users to navigate

complex environments, such as shopping centers and

airports, [21]. This can save time for users and make

the navigation process more efficient. In addition, the

use of AR-based wayfinding systems can enhance

visitors' overall experience in indoor shopping malls

and other public spaces, [58]. This can create a

positive perception of the technology and increase

the likelihood of adoption. This can create a positive

perception of the technology and increase the

likelihood of adoption. Moreover, AR-based

wayfinding systems can improve wayfinding

performance in existing healthcare facilities, [59].

This can help patients and visitors navigate

healthcare facilities more easily and efficiently.

WSEAS TRANSACTIONS on COMPUTER RESEARCH

DOI: 10.37394/232018.2024.12.4

Ahmad Said, Zun Er Ang, Yulita Hanum P. Iskandar

E-ISSN: 2415-1521

Volume 12, 2024

Wayfinding can help create a more equitable

environment and improve social cohesion by

supporting local amenities and designing information

to be accessed by all, [60]. This can benefit the

community as a whole.

6 Implications

The objective of this research was to identify the

direct effect between the independent variables and

the user's behavioral intention to use the indoor AR

wayfinding technology. In terms of theoretical

contributions, this study helps the readers or new

researchers to better grasp the general factors

affecting the intention to use the indoor AR

wayfinding system application by using the UTAUT

theory. To the best of the researcher’s knowledge,

this research is one of the first study in of indoor AR

wayfinding system applications in Singapore by

using a base theoretical framework to investigate the

market. This study could also serve as a reference

for future researchers with the technology topic

related to location-based services, mobile banking

services, augmented reality, and wayfinding

services.

This research helps to provide insights into the

strategic development of digital leaders in Singapore

to lead and ensure the success of the digital

transformation aligned with the ultimate goal of

making Singapore a regional market producer. As a

leader, marketers, and brand managers in

developing technology systems, there is a need to

establish market positioning not only based on the

current trends but also include useful niche markets

that will potentially allow them a competitive

advantage over other businesses or countries. Hence

this study will help to raise the awareness of the

indoor wayfinding application in Singapore. Service

providers and/or application developers can design

the application system to have more innovative

features and also include the factors that have been

established in the results to enhance and fulfill

consumers’ demands.

7 Conclusion

This study endeavored to analyze the direct

connection between factors affecting the intention to

use the indoor AR wayfinding system application by

using the UTAUT theory. The findings have

uncovered that facilitating conditions, and

performance expectancy have a significant positive

relationship to influencing user’s behavioral

intention to use indoor AR wayfinding system

applications. Along with providing the necessary

assistance, these aspects will particularly enhance

the likelihood of adopting the AR wayfinding

system in an indoor retail context.

8 Limitations and Future Studies

Three limitations can be identified in this study. First,

there are not many AR wayfinding navigation

applications available on the app store. It is

recommended that researchers use qualitative and

quantitative techniques as a mixed method to have a

more in-depth understanding. Next is about the

location. The current study is in Singapore but

without any particular focus area. Future research

could be based on a specific location such as a central

business district or central area of an upscale

shopping area in Singapore to compare AR

wayfinding across different demographics of users

(business use versus casual use) to increase the

variety of perspectives and to improve validity.

Lastly, the current study primarily collected data

from people living in urban areas who were able to

operate the AR wayfinding application with their

ICT knowledge and skills. To capture a more holistic

view, future studies should also include respondents

who live in rural areas.

References:

[1] J. Ženka, J. Macháček, L. Krtička, P. Michna,

and P. Kořízek, "Acceptance of a smartphone

navigation application by hospital patients and

visitors: the role of gender, age, and

WSEAS TRANSACTIONS on COMPUTER RESEARCH

DOI: 10.37394/232018.2024.12.4

Ahmad Said, Zun Er Ang, Yulita Hanum P. Iskandar

E-ISSN: 2415-1521

Volume 12, 2024

education," Hungarian Geographical Bulletin,

vol. 70, no. 2, pp. 149-161, 2021.

[2] J. Saretzki, J. Pretsch, G. Obertaxer, and R.

Lesjak, "Security that Matters: General

Knowledge and Correlations in the Context of

Applied Satellite Navigation, Specific

Interference-Events and the Use of the

GNSS-Technology," European Journal of

Social Sciences, vol. 5, no. 1, pp. 27-44, 2022.

[3] Y. A. Sekhavat and J. Parsons, "The effect of

tracking technique on the quality of user

experience for augmented reality mobile

navigation," Multimedia Tools Applications,

vol. 77, no. 10, pp. 11635-11668, 2018.

[4] R. Chen, P. Perry, R. Boardman, and H.

McCormick, "Augmented reality in retail: a

systematic review of research foci and future

research agenda," International Journal of

Retail Distribution Management, vol. 50, no. 4,

pp. 498-518, 2022.

[5] S. G. Dacko, "Enabling smart retail settings via

mobile augmented reality shopping apps,"

Technological forecasting social change, vol.

124, pp. 243-256, 2017.

[6] K. Chopra, "Indian shopper motivation to use

artificial intelligence: Generating Vroom’s

expectancy theory of motivation using

grounded theory approach," International

Journal of Retail Distribution Management,

vol. 47, no. 3, pp. 331-347, 2019.

[7] F. Baytar, T. Chung, and E. Shin, "Evaluating

garments in augmented reality when shopping

online," Journal of Fashion Marketing

Management: An International Journal, vol.

24, no. 4, pp. 667-683, 2020.

[8] D. Plotkina and H. Saurel, "Me or just like me?

The role of virtual try-on and physical

appearance in apparel M-retailing," Journal of

Retailing Consumer Services, vol. 51, pp.

362-377, 2019.

[9] A. Javornik, "'It's an illusion, but it looks

real!'Consumer affective, cognitive and

behavioral responses to augmented reality

applications," Journal of Marketing

Management, vol. 32, no. 9-10, pp. 987-1011,

2016.

[10] A. Poushneh and A. Z. Vasquez-Parraga,

"Discernible impact of augmented reality on

retail customer's experience, satisfaction and

willingness to buy," Journal of Retailing

Consumer Services, vol. 34, pp. 229-234,

2017.

[11] J. Scholz and K. Duffy, "We ARe at home:

How augmented reality reshapes mobile

marketing and consumer-brand relationships,"

Journal of Retailing Consumer Services, vol.

44, pp. 11-23, 2018.

[12] A. R. Smink, E. A. Van Reijmersdal, G. Van

Noort, and P. C. Neijens, "Shopping in

augmented reality: The effects of spatial

presence, personalization and intrusiveness on

the app and brand responses," Journal of

Business Research, vol. 118, pp. 474-485,

2020.

[13] T. Hilken, K. de Ruyter, M. Chylinski, D. Mahr,

and D. I. Keeling, "Augmenting the eye of the

beholder: exploring the strategic potential of

augmented reality to enhance online service

experiences," Journal of the Academy of

Marketing Science, vol. 45, pp. 884-905, 2017.

[14] M. Y.-C. Yim, S.-C. Chu, and P. L. Sauer, "Is

augmented reality technology an effective tool

for e-commerce? An interactivity and

vividness perspective," Journal of interactive

marketing, vol. 39, no. 1, pp. 89-103, 2017.

[15] A. Jessen et al., "The playground effect: How

augmented reality drives creative customer

engagement," Journal of Business Research,

vol. 116, pp. 85-98, 2020.

[16] B. Romano, S. Sands, and J. I. Pallant,

"Augmented reality and the customer journey:

an exploratory study," Australasian Marketing

Journal, vol. 29, no. 4, pp. 354-363, 2021.

[17] M. Y.-C. Yim and S.-Y. Park, "I am not

satisfied with my body, so I like augmented

reality (AR)": Consumer responses to

AR-based product presentations," Journal of

Business Research, vol. 100, pp. 581-589,

2019.

[18] M. Chylinski, J. Heller, T. Hilken, D. I.

WSEAS TRANSACTIONS on COMPUTER RESEARCH

DOI: 10.37394/232018.2024.12.4

Ahmad Said, Zun Er Ang, Yulita Hanum P. Iskandar

E-ISSN: 2415-1521

Volume 12, 2024

Keeling, D. Mahr, and K. de Ruyter,

"Augmented reality marketing: A

technology-enabled approach to situated

customer experience," Australasian Marketing

Journal, vol. 28, no. 4, pp. 374-384, 2020.

[19] P. A. Rauschnabel, J. He, and Y. K. Ro,

"Antecedents to the adoption of augmented

reality smart glasses: A closer look at privacy

risks," Journal of Business Research, vol. 92,

pp. 374-384, 2018.

[20] S. T. Board. (2021). Building Immersive

Worlds with AR Available:

https://www.stb.gov.sg/content/stb/en/trade-ev

ents-and-resources/Tcube/stories/technology/t

ech-ar.html (Accessed date: 28/09/2023)

[21] M. J. Kim, X. Wang, S. Han, and Y. Wang,

"Implementing an augmented reality-enabled

wayfinding system through studying user

experience and requirements in complex

environments," Visualization in Engineering,

vol. 3, pp. 1-12, 2015.

[22] J. Zhang, X. Xia, R. Liu, and N. Li,

"Enhancing human indoor cognitive map

development and wayfinding performance

with immersive augmented reality-based

navigation systems," Advanced Engineering

Informatics, vol. 50, p. 101432, 2021.

[23] A. Lo Valvo, D. Croce, D. Garlisi, F. Giuliano,

L. Giarré, and I. Tinnirello, "A navigation and

augmented reality system for visually impaired

people," Sensors, vol. 21, no. 9, p. 3061, 2021.

[24] T. Zhou, P. Xia, Q. Zhu, and J. Du,

"Cognition-driven navigation assistive system

for emergency indoor wayfinding (CogDNA):

proof of concept and evidence," Safety Science,

vol. 162, p. 106100, 2023.

[25] A. S. Ahadzadeh, S. P. Sharif, F. S. Ong, and K.

W. Khong, "Integrating health belief model

and technology acceptance model: an

investigation of health-related Internet use,"

Journal of medical Internet research, vol. 17,

no. 2, p. e3564, 2015.

[26] S. Nasir and Y. Yurder, "Consumers’ and

physicians’ perceptions about high tech

wearable health products," Procedia-Social

Behavioral Sciences, vol. 195, pp. 1261-1267,

2015.

[27] G. Becker, "The age of human capital.

Education in the Tweny-First Century," ed:

Retrieved from the World Wide Web:

http://economics, 2010 (Accessed date:

28/09/2023).

[28] E. Park, K. J. Kim, and S. J. Kwon,

"Understanding the emergence of wearable

devices as next-generation tools for health

communication," Information Technology

People, vol. 29, no. 4, pp. 717-732, 2016.

[29] H. Yun, D. Han, and C. Lee, "Extending

UTAUT to predict the use of location-based

services," 2011.

[30] P. K. Beh, Y. Ganesan, M. Iranmanesh, and B.

Foroughi, "Using smartwatches for fitness and

health monitoring: the UTAUT2 combined

with threat appraisal as moderators,"

Behaviour Information Technology, vol. 40, no.

3, pp. 282-299, 2021.

[31] R. de Sena Abrahão, S. N. Moriguchi, and D. F.

Andrade, "Intention of adoption of mobile

payment: An analysis in the light of the

Unified Theory of Acceptance and Use of

Technology (UTAUT)," RAI Revista de

Administração e Inovação, vol. 13, no. 3, pp.

221-230, 2016.

[32] Y. K. Dwivedi, N. P. Rana, A. Jeyaraj, M.

Clement, and M. D. Williams, "Re-examining

the unified theory of acceptance and use of

technology (UTAUT): Towards a revised

theoretical model," Information Systems

Frontiers, vol. 21, pp. 719-734, 2019.

[33] M. D. Williams, N. P. Rana, and Y. K. Dwivedi,

"The unified theory of acceptance and use of

technology (UTAUT): a literature review,"

Journal of enterprise information management,

vol. 28, no. 3, pp. 443-488, 2015.

[34] I. A. Junglas and R. T. Watson,

"Location-based services," Communications of

the ACM, vol. 51, no. 3, pp. 65-69, 2008.

[35] A. Rahman, A. Khan, and A. A. Raza,

"Parkinson’s disease detection based on signal

processing algorithms and machine learning,"

WSEAS TRANSACTIONS on COMPUTER RESEARCH

DOI: 10.37394/232018.2024.12.4

Ahmad Said, Zun Er Ang, Yulita Hanum P. Iskandar

E-ISSN: 2415-1521

Volume 12, 2024

CRPASE: Transactions of Electrical,

Electronic Computer Engineering, vol. 6, pp.

141-145, 2020.

[36] I. Ajzen, "The theory of planned behavior,"

Organizational behavior human decision

processes, vol. 50, no. 2, pp. 179-211, 1991.

[37] V. Venkatesh, M. G. Morris, G. B. Davis, and F.

D. Davis, "User acceptance of information

technology: Toward a unified view," MIS

Quarterly, pp. 425-478, 2003.

[38] C. Schapsis, L. Chiagouris, and N. C. Pham,

"Are Consumers Ready for Augmented

Reality? Factors Influencing Online Footwear

Purchasing Intentions Using AR Technology,"

Journal of Marketing Development

Competitiveness, vol. 15, no. 2, pp. 21-36,

2021.

[39] V. Venkatesh, J. Y. Thong, and X. Xu,

"Consumer acceptance and use of information

technology: extending the unified theory of

acceptance and use of technology," MIS

Quarterly, pp. 157-178, 2012.

[40] J. Frith, "Turning life into a game: Foursquare,

gamification, and personal mobility," Mobile

Media Communication, vol. 1, no. 2, pp.

248-262, 2013.

[41] D.-i. D. Han, M. C. Tom Dieck, and T. Jung,

"Augmented Reality Smart Glasses (ARSG)

visitor adoption in cultural tourism," Leisure

Studies, vol. 38, no. 5, pp. 618-633, 2019.

[42] X. Guo, Y. Sun, Z. Yan, and N. Wang,

"Privacy-personalization paradox in the

adoption of mobile health service: the

mediating role of trust," 2012.

[43] P. Samuelson, "Information law and policy

video lectures," UC California, Berkeley,

Berkeley, CA, 2008.

[44] D. E. Team. (2023, September, 2023). What is

construct validity? Available:

https://dovetail.com/research/construct-validit

y/ (Accessed date: 28/09/2023)

[45] P. Bhandari, Construct validity, definition,

types & examples. Scribbr, 2022.

[46] Q. Yang, C. Pang, L. Liu, D. C. Yen, and J. M.

Tarn, "Exploring consumer perceived risk and

trust for online payments: An empirical study

in China’s younger generation," Computers in

human behavior, vol. 50, pp. 9-24, 2015.

[47] Y. Gao, H. Li, and Y. Luo, "An empirical study

of wearable technology acceptance in

healthcare," Industrial Management Data

Systems, 2015.

[48] L.-H. Wu, L.-C. Wu, and S.-C. Chang,

"Exploring consumers’ intention to accept

smartwatch," Computers in Human Behavior,

vol. 64, pp. 383-392, 2016.

[49] J. Henseler, C. M. Ringle, and M. Sarstedt, "A

new criterion for assessing discriminant

validity in variance-based structural equation

modeling," Journal of the Academy of

Marketing Science, vol. 43, pp. 115-135, 2015.

[50] J. F. Hair Jr, M. Sarstedt, C. M. Ringle, and S. P.

Gudergan, Advanced issues in partial least

squares structural equation modeling. Sage

Publications, 2017.

[51] P. M. Podsakoff, S. B. MacKenzie, J.-Y. Lee,

and N. P. Podsakoff, "Common method biases

in behavioral research: a critical review of the

literature and recommended remedies,"

Journal of applied psychology, vol. 88, no. 5, p.

879, 2003.

[52] S. Streukens and S. Leroi-Werelds,

"Bootstrapping and PLS-SEM: A step-by-step

guide to getting more out of your bootstrap

results," European Management Journal, vol.

34, no. 6, pp. 618-632, 2016.

[53] N. A. A. Shamsuddin and S. C. Din, "Spatial

ability skills: A correlation between

Augmented Reality (AR) and conventional

way on wayfinding system,"

Environment-Behaviour Proceedings Journal,

vol. 1, no. 2, pp. 159-167, 2016.

[54] R. Tahir and J. Krogstie, "Impact of Navigation

Aid and Spatial Ability Skills on Wayfinding

Performance and Workload in Indoor-Outdoor

Campus Navigation: Challenges and Design,"

Applied Sciences, vol. 13, no. 17, p. 9508,

2023.

[55] K. Takács, A. Flache, and M. Mäs,

"Discrepancy and disliking do not induce

WSEAS TRANSACTIONS on COMPUTER RESEARCH

DOI: 10.37394/232018.2024.12.4

Ahmad Said, Zun Er Ang, Yulita Hanum P. Iskandar

E-ISSN: 2415-1521

Volume 12, 2024

negative opinion shifts," PloS one, vol. 11, no.

6, p. e0157948, 2016.

[56] Designveloper. (2023, September, 20th). Top 5

App Development Companies in Singapore.

Available:

https://www.designveloper.com/blog/app-deve

lopment-in-singapore/ (Accessed date:

28/09/2023)

[57] C. L. Charlotte Trueman. (2019, September,

20). How Singapore is using artificial

intelligence. Available:

https://www.cio.com/article/221994/how-sing

apore-is-using-artificial-intelligence.html

(Accessed date: 28/09/2023)

[58] U. Project. (2023, September, 20th).

Augmented reality wayfinding: The future of

indoor navigation. Available:

https://untitledproject.sg/augmented-reality-w

ayfinding-the-future-of-indoor-navigation/

(Accessed date: 28/09/2023)

[59] A. Al-Sharaa, M. Adam, A. S. Amer Nordin, A.

Alhasan, R. Mundher, and O. Zaid,

"Enhancing Wayfinding Performance in

Existing Healthcare Facilities Using Virtual

Reality Environments to Revise the

Distribution of Way-Showing Devices,"

Buildings, vol. 12, no. 6, p. 790, 2022.

[60] Seattle. (2023, September, 20th). Pedestrian

Wayfinding Program. Available:

https://www.seattle.gov/documents/Departme

nts/SDOT/UrbanDesignProgram/PedestrianW

ayfindingProgram/Wayfinding%20Strategy_J

uly2019_SDOT%20Edit.pdf (Accessed date:

28/09/2023)

WSEAS TRANSACTIONS on COMPUTER RESEARCH

DOI: 10.37394/232018.2024.12.4

Ahmad Said, Zun Er Ang, Yulita Hanum P. Iskandar

E-ISSN: 2415-1521

Volume 12, 2024

APPENDIX

Table 1. Measurement Items in Questionnaire

Variable(s)

Measurement Items Adopted from

Performance

Expectancy

I find the AR wayfinding application useful for indoor shopping navigation.

[47]

Using an AR wayfinding application would enable me to take action related to my indoor

shopping navigation more quickly.

Using the AR wayfinding application improves the quality of my indoor shopping navigation.

Effort

Expectancy

Learning how to use AR wayfinding applications for indoor shopping navigation is easy for me.

[47]

I find it an easy-to-use AR wayfinding application for indoor shopping navigation.

It is easy for me to become skillful at using AR wayfinding applications for indoor shopping

navigation.

Social

Influence

People who are important to me would think that I should use an AR Wayfinding application for

indoor shopping navigation.

[47]

People who influence me would think that I should use an AR Wayfinding application for indoor

shopping navigation.

People whose opinions are valued by me would prefer that I use an AR Wayfinding application

for indoor shopping navigation.

Facilitating

Conditions

I have the resources necessary to use AR wayfinding applications for indoor shopping navigation.

[39]

I have the knowledge necessary to use AR wayfinding applications for indoor shopping

navigation.

AR wayfinding application is compatible with other technologies I use.

I can get help from others when I have difficulties using AR wayfinding applications for indoor

shopping navigation.

Privacy

Risk

By using an AR wayfinding application, I am at risk of infringement of my privacy.

[46]

By using an AR wayfinding application, I am at risk of my personal information being collected

excessively.

By u

sing an AR wayfinding application, my personal information is at risk of being accessed by

unauthorized people.

By using an AR wayfinding application, my actions are at risk of being tracked and monitored.

Behavioral

Intention

I would be willing to use an AR Wayfinding application for indoor shopping navigation.

[48]

I would be willing to use an AR Wayfinding application for indoor shopping navigation if I

possess one.

I would be willing to let an AR Wayfinding application help me navigate indoor shopping.

WSEAS TRANSACTIONS on COMPUTER RESEARCH

DOI: 10.37394/232018.2024.12.4

Ahmad Said, Zun Er Ang, Yulita Hanum P. Iskandar

E-ISSN: 2415-1521

Volume 12, 2024

Table 2. Summary of Demographic Profile of the Respondents

Variables Category Frequency

Gender Male 119 68.0%

Female 56 32.0%

Age

18 - 29 58 33.1%

30 - 39 92 52.6%

40 - 49 21 12.0%

50 and above 4 2.3%

Education

Secondary 22 12.6%

High School or Diploma 67 38.3%

Bachelor’s Degree 61 34.9%

Postgraduate Qualifications

25 14.2%

What kind of smart devices do you use often

iOS 73 42%

Android 88 50%

Microsoft 14 8%

Navigation Application Use Frequency (Non-AR)

Multiple Times Daily 57 32.6%

Once Daily 67 38.3%

Multiple Times Weekly 30 17.1%

Once Weekly 11 6.3%

Once A Month 10 5.7%

Confidence in Mobile Application Use

Extremely Confident 64 36.6%

Confident 79 45.1%

Somewhat Confident 30 17.2%

Not Confident 2 1.1%

Extremely Not Confident 0 0.0%

WSEAS TRANSACTIONS on COMPUTER RESEARCH

DOI: 10.37394/232018.2024.12.4

Ahmad Said, Zun Er Ang, Yulita Hanum P. Iskandar

E-ISSN: 2415-1521

Volume 12, 2024

Table 4. Convergent Validity

Variable(s) Items Loading CA RhoA CR AVE

BehInt

0.905 0.908 0.941 0.841

BI1 0.912

BI2 0.926

BI3 0.912

EffExp

0.871 0.871 0.921 0.794

EE1 0.892

EE2 0.897

EE3 0.849

FacCon

0.899 0.899 0.930 0.768

FC1 0.861

FC2 0.902

FC3 0.914

FC4 0.825

PerExp

0.813 0.812 0.889 0.728

PE1 0.830

PE2 0.879

PE3 0.849

PriRisk

0.940 0.960 0.957 0.848

PR1 0.866

PR2 0.930

PR3 0.959

PR4 0.926

SocInf

0.828 0.875 0.898 0.748

SI1 0.924

SI2 0.937

SI3 0.717

WSEAS TRANSACTIONS on COMPUTER RESEARCH

DOI: 10.37394/232018.2024.12.4

Ahmad Said, Zun Er Ang, Yulita Hanum P. Iskandar

E-ISSN: 2415-1521

Volume 12, 2024

Table 5. Total Variance Explained using Principal Component Analysis (PCA)

Component Initial Eigenvalues Extraction Sums of Squared Loadings

Total % of Variance Cumulative % Total % of Variance Cum %

1 9.069 45.345 45.345 9.069 45.345 45.345

2 2.759 13.797 59.141 2.759 13.797 59.141

3 1.620 8.100 67.241 1.620 8.100 67.241

4 1.053 5.267 72.508 1.053 5.267 72.508

5 0.861 4.303 76.811 0.861 4.303 76.811

6 0.717 3.587 80.398 0.717 3.587 80.398

7 0.595 2.975 83.372 0.595 2.975 83.372

8 0.529 2.646 86.018 0.529 2.646 86.018

9 0.405 2.024 88.042 0.405 2.024 88.042

10 0.379 1.893 89.935 0.379 1.893 89.935

11 0.318 1.588 91.523 0.318 1.588 91.523

12 0.270 1.350 92.873 0.270 1.350 92.873

13 0.254 1.269 94.143 0.254 1.269 94.143

14 0.245 1.225 95.368 0.245 1.225 95.368

15 0.222 1.110 96.478 0.222 1.110 96.478

16 0.206 1.028 97.505 0.206 1.028 97.505

17 0.167 0.837 98.342 0.167 0.837 98.342

18 0.130 0.651 98.993 0.130 0.651 98.993

19 0.126 0.628 99.622 0.126 0.628 99.622

20 0.076 0.378 100 0.076 0.378 100

Table 6. Direct Effects

Beta Sample Mean (M) Standard Deviation (STDEV) T Values P Values Decision

FacCon -> BehInt 0.331 0.337 0.092 3.599 0.000 Supported

PerExp -> BehInt 0.383 0.378 0.097 3.960 0.000 Supported

EffExp -> BehInt 0.160 0.140 0.099 1.623 0.052 Not Supported

SocInf -> BehInt 0.061 0.068 0.100 0.608 0.272 Not

Supported

PriRisk -> BehInt -0.023 -0.011 0.070 0.325 0.373 Not

Supported

WSEAS TRANSACTIONS on COMPUTER RESEARCH

DOI: 10.37394/232018.2024.12.4

Ahmad Said, Zun Er Ang, Yulita Hanum P. Iskandar

E-ISSN: 2415-1521

Volume 12, 2024

Table 7. Hypotheses Testing Results

Hypothesis Structure

Path Hypothesis Statement Finding

H1. FC → BI Facilitating Conditions positively influence the Behavioral Intention of adopting an AR

wayfinding system. Accepted

H2. PE → BI Performance Expectancy positively influences the Behavioral Intention of adopting an AR

wayfinding system. Accepted

H3. EE → BI Effort Expectancy positively influences the Behavioral Intention of adopting an AR

wayfinding system. Rejected

H4. SI → BI

Social Influence positively influences the Behavioral Intention of adopting an AR wayfinding

system. Rejected

H5. PR → BI Privacy Risk negatively influences the Behavioral Intention of adopting an AR wayfinding

system. Rejected

Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

The authors equally contributed to the present

research, at all stages from the formulation of the

problem to the final findings and solution.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

Bina Nusantara University supported this study.

Conflict of Interest

The authors have no conflict of interest to declare.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

_US

WSEAS TRANSACTIONS on COMPUTER RESEARCH

DOI: 10.37394/232018.2024.12.4

Ahmad Said, Zun Er Ang, Yulita Hanum P. Iskandar

E-ISSN: 2415-1521

Volume 12, 2024