augmented reality research paper 2022

Augmented Reality Technology: Current Applications, Challenges and its Future

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• Published: 26 June 2023

The impact of augmented reality on student attitudes, motivation, and learning achievements—a meta-analysis (2016–2023)

• Wenwen Cao 1 &
• Zhonggen Yu   ORCID: orcid.org/0000-0002-3873-980X 2  

Humanities and Social Sciences Communications volume  10 , Article number:  352 ( 2023 ) Cite this article

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• Science, technology and society

In light of the COVID-19 pandemic, a significant number of students have been compelled to remain at home while receiving education supported by augmented reality (AR) technologies. To determine the impact of AR technologies on educational outcomes, the present study undertook a meta-analysis utilizing Stata/MP 14.0. The study found that the attitudes of learners towards AR-assisted education were more positive, and their learning achievements were significantly higher compared to those who did not use AR technologies. However, there was no significant difference in motivation levels between the AR-assisted and non-AR-assisted educational models. The researchers explored several reasons for this result, but they could not identify any clear explanation. Future studies could take into account other factors that might affect education outcomes such as learning styles and learner personality. Doing so could shed more light on the impact of AR technologies on education.

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Introduction

Since the emergence of the COVID-19 pandemic, many students have been compelled to receive education from home with the assistance of augmented reality (AR) technologies (Saleem et al., 2021 ). Given the rising popularity of AR technologies in the field of education (Tezer et al., 2019 ), a multitude of studies have conducted meta-analyses to investigate their effectiveness, particularly under the COVID-19 pandemic context (e.g., Selek and Kiymaz, 2020 ; Bork et al., 2020 ; Gargrish et al., 2021 ; Gonzalez et al., 2020 ). One recent meta-analysis found that AR technologies could have a positive impact on learning outcomes when users’ spatial abilities were taken into account (Bölek et al., 2021 ). While medium-sized effects were often observed in terms of learning gains resulting from the use of AR (Garzón and Acevedo, 2019 ), the results may have been influenced by the exclusion of studies with insufficient data. Additionally, when applied in collaborative learning, AR technologies could have a major influence on learning outcomes, although the results were limited to the pedagogical methods utilized in the included sample (Garzón et al., 2020 ).

The field of education has witnessed a rapid surge in the popularity of augmented reality (AR), which has the potential to greatly enhance learning experiences (Garzón et al., 2019 ). However, the study conducted by Garzón et al. ( 2019 ) neglected to define the specific features of AR that can conveniently assist and improve learning achievements. When compared to traditional learning methods, AR-assisted learning has demonstrated a considerable improvement in learning achievements, and the efficacy of various AR applications in education has shown no significant differences (Ozdemir et al., 2018 ). It is important to note, however, that the sample size in Ozdemir et al.’s study was restricted to only 16 participants and was limited to the Social Sciences Citation Index, resulting in a possible sample bias that could impede the reliability of their results. Learner attitudes toward and learning achievements in AR-assisted education may need further examination since both variables have not received enough exploration.

A meta-analysis of AR-assisted education offers several advantages (Cao and Hsu, 2022 ). Combining the results of multiple studies increases the sample size and statistical power, enabling more accurate and dependable conclusions in AR-assisted education. By analyzing multiple studies together, meta-analysis can identify patterns and trends that may not be apparent in individual studies, indicating the consistency of results across different studies and enhancing the generalizability of findings. Meta-analysis mitigates the impact of bias in individual studies by examining a larger pool of data and reduces the need for replication studies, thereby saving valuable time and resources. It also helps integrate findings with existing theoretical frameworks, providing a more comprehensive understanding of the topic in AR-assisted education. Overall, meta-analysis provides a more robust evidence base for decision-making in policy and practice in AR-assisted education.

The purpose of this meta-analysis is to investigate the impact of Augmented Reality (AR) on educational outcomes while minimizing the aforementioned limitations. We intend to achieve this by incorporating a larger sample size from diverse databases. Our study aims to address the issue of sample bias by expanding the sample size and examining the role of AR features in education. We will include all available studies related to AR, and in cases where adequate information is unavailable, we will reach out to the authors for clarification. Our analysis will also encompass various pedagogical approaches facilitated by AR technologies, with the goal of arriving at comprehensive conclusions regarding attitudes, learning achievements, and motivation.

Literature review

Attitudes toward ar used for education.

The utilization of augmented reality (AR) has been suggested as a means to enhance attitudes towards and satisfaction with education. As reported by Weng et al. ( 2020 ), AR has the potential to induce positive attitudes toward education. Alqarni ( 2021 ) suggests that AR may facilitate positive learning experiences, including academic achievements for students with disabilities. The integration of AR into problem-based learning has also been noted as a promising approach to improving students’ attitudes toward specific subjects (Fidana and Tuncel, 2019 ). Recent research conducted by Sahin and Yilmaz ( 2020 ) found that students who utilized an AR-enhanced science course, specifically “Solar System and Beyond,” exhibited more favorable attitudes toward learning than their non-AR-using peers. Additionally, they reported higher levels of satisfaction and lower levels of anxiety. Delello ( 2014 ) also posits that AR technologies may play a crucial role in improving attitudes toward AR-assisted education.

Despite the potential benefits of AR technology in enhancing attitudes toward education, it is important to acknowledge that some studies have reported negative attitudes toward its use. For instance, Basoglu et al. ( 2018 ) suggest that the use of AR smart glasses (ARSGs) may pose privacy concerns and reduce the perceived ease of use, which can lead to negative attitudes toward AR. Similarly, Akçayır et al. ( 2016 ) assert that students’ lack of familiarity with AR technology can result in frustration and generate negative attitudes toward AR-assisted education. Given the contradictory findings surrounding the impact of AR on attitudes toward education, we propose an alternative hypothesis for further investigation.

H1: The attitudes of learners towards AR-assisted education are significantly more positive compared to those without the aid of AR technologies.

Learning achievements

The majority of studies have reported positive learning outcomes associated with the use of AR technologies. Akçayır and Akçayır ( 2017 ) suggested that utilizing AR technology could enhance learning achievements, foster student engagement, and boost confidence in academic activities. Fidana and Tuncel ( 2019 ) found that integrating AR technologies into problem-based learning approaches resulted in improved learning achievements. Similarly, Sahin and Yilmaz ( 2020 ) reported that students who used AR technologies achieved significantly higher learning outcomes than those who did not. Lee and Hsu ( 2021 ) also demonstrated the efficacy of AR-assisted learning through the “Makeup AR” approach, which enhanced learning achievements, self-efficacy, and reduced cognitive loads. Wu et al. ( 2018 ) further supported the effectiveness of AR-based learning systems, reporting significantly better learning achievements compared to traditional learning methods.

Several studies have reported negative learning outcomes associated with augmented reality (AR) technologies. For instance, Kuhn and Lukowicz ( 2016 ) found that incorporating AR technologies, such as Google Glass, into intelligent classes did not result in significantly higher learning achievements compared to those without AR technologies. Conversely, students who learned using a serious game with AR technologies called Lost in Space demonstrated significantly greater improvements in learning achievements than those who used traditional learning tools, but no significant differences were observed during gameplay (Hou et al., 2021 ). Additionally, AR technologies could potentially have adverse effects on mobile learning achievements, as improper mobile design with AR technologies may lead to frustrating learning outcomes and reduced learning efficiency (Chu, 2014 ; Hwang et al., 2016 ). Given these contradictory results, we propose an alternative hypothesis.

H2. Learning achievements in AR-assisted education exhibit significantly higher results compared to those achieved through non-AR-assisted education.

Motivation of AR technology-assisted learning

Numerous studies have demonstrated that augmented reality (AR) technologies can enhance learning motivation. For example, Cavallo and Laubach ( 2001 ) found that AR technologies could improve learning motivation. Akçayır and Akçayır ( 2017 ) reported that AR technologies motivated students to participate in learning activities. Yildirim ( 2016 ) discovered that students who used computer-based AR technologies were significantly more motivated than the control group who did not use AR technologies. Moreover, Tian et al. ( 2014 ) and Zhang et al. ( 2014 ) indicated that the use of AR technologies in education effectively enhanced students’ motivation. Cen et al. ( 2020 ) observed that a mobile AR-based learning system significantly improved the motivation of secondary chemistry learners. Demitriadou et al. ( 2020 ) suggested that AR technologies were effective in increasing learning motivation.

Despite the positive effects of augmented reality (AR) technologies on learning motivation, some previous studies have shown differing results. For instance, Gómez-García et al. ( 2021 ) found that students who used AR technologies did not exhibit significantly higher learning motivation than those who did not use them. Additionally, Lee and Hsu ( 2021 ) reported that the application of AR in vocational certification courses failed to significantly enhance learning motivation. Furthermore, teachers who resist changing their traditional pedagogical approaches may feel less motivated by AR technologies, which could also dampen students’ motivation for using AR technologies in learning. Similarly, students who are accustomed to traditional learning styles may also exhibit resistance toward AR-assisted learning. Given these implications and inconsistent findings, we propose an alternative hypothesis.

H3. Learning motivation in AR-assisted education shows a substantial increase compared to non-AR-assisted education.

Research methods

This meta-analysis adhered strictly to the protocols outlined by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, as detailed by Page et al. ( 2021 ). PRISMA outlined 27 items that served as a guide throughout the meta-analysis process and provides specific recommendations for conducting a thorough and valid meta-analysis. The ethical committee overseeing this study has granted a waiver for registration, as the study does not involve any human participants and does not violate any ethical criteria.

Eligibility criteria

Following the PRISMA protocol, we established explicit inclusion and exclusion criteria for selecting relevant studies. Inclusion criteria were as follows: (1) large randomized controlled trials that involved AR technology-assisted education and conducted comparative studies; (2) written in English language; and (3) formally and openly published, and peer-reviewed. We excluded studies that (1) focused solely on AR technology without any reference to education; (2) lacked sufficient information for meta-analyses; (3) belonged to the category of review studies; (4) had no relevance to the study topic; (5) were of overall lower quality based on Standards for Reporting on Empirical Social Science Research in AERA Publications; (6) contained insufficient data; (7) had small sample sizes; or (8) yielded unconvincing results.

Search strategy and selection process

The study involved conducting a systematic search of online databases, including Web of Science, Scopus, Wiley, Taylor & Francis, ScienceDirect Elsevier, and SpringerNature, using specific syntactic rules to enter keywords such as “AR, augmented reality, education, control group, experimental group, learning, and teaching”. Prior to the screening, duplicates, records deemed ineligible by automation tools, and those with missing information, small sample sizes, lower quality, lack of sufficient data, or unconvincing conclusions were removed. The selection process was reviewed independently by two researchers, achieving satisfactory inter-rater consistency ( k  = 0.87). In cases of disagreement, a third reviewer was consulted. Ultimately, 28 relevant results were included after screening and excluding ineligible literature (see Fig. 1 ).

A flowchart of the literature inclusion procedure.

Characteristics of the included studies

The present review encompasses studies that showcase the recent accomplishments in AR-assisted education, with publications ranging from 2016 to 2023. The cumulative number of participants in the control group is 1509, while the experimental group consists of 1417 individuals. These studies investigate the comparative effectiveness of AR-assisted and traditional educational approaches in terms of learning achievements, learners’ attitudes, and motivation. All included research articles are published in distinguished journals such as Advances in Physiology Education, Australasian Journal of Educational Technology, Behaviour & Information Technology, British Journal of Educational Technology, Computer Application Engineering Education, Computers & Education, Computers in Human Behavior, Education Sciences, IEEE Transactions on Learning Technologies, Innovation in Language Learning and Teaching, Interactive Learning Environments, International Journal of Human–Computer Interaction, Journal of Baltic Science Education, Journal of Computer Assisted Learning, Journal of Science Education and Technology, and Universal Access in the Information Society (refer to Table 1 ).

Data synthesis

In order to ensure the reliability of our findings, we employed two methods: publication bias testing and sensitivity analyses. Publication bias is a common issue in research, as journals tend to prioritize publishing positive results over negative ones. To detect potential publication bias, we utilized Begg’s (Begg and Mazumdar, 1994 ) and Egger’s tests (Egger et al., 1997 ). We also examined the distribution of individual studies to identify any presence or absence of publication bias. Additionally, we performed sensitivity analyses using Stata/MP 14.0 software to further validate our results.

Begg’s and Egger’s tests are two commonly used statistical methods to assess publication bias in meta-analyses. Begg’s test is a rank correlation test that examines the association between effect sizes and their variances or standard errors. A non-significant p -value (e.g., p  > 0.05) suggests that there is no evidence of publication bias. However, a significant p -value (e.g., p  < 0.05) may indicate the presence of publication bias, but it can also mean that the sample size is too small or the number of studies included in the analysis is too few. Egger’s test is a linear regression test that examines the association between the effect sizes and their precision (the reciprocal of variance). A non-significant p -value (e.g., p  > 0.05) indicates that there is no evidence of publication bias. However, a significant p -value (e.g., p  < 0.05) suggests the presence of publication bias, but it can also mean that the sample size is too small, or there is substantial heterogeneity among the included studies.

The present meta-analysis was conducted using Stata/MP 14.0 software. Firstly, we extracted data pertaining to mean values, standard deviations, and participant numbers across both experimental and control groups. Additionally, subgroups such as learning achievements, attitudes, and motivation in AR-assisted education were also extracted. Effect sizes were then calculated using Cohen’s d formula: d  = Me−Mc/Sp, where Me represents the means of the experimental group, Mc represents the means of the control group, and Sp signifies the pooled standard deviation of both groups (Sedgwick and Marston, 2013 ). We will classify effect size values as very small if they are around 0.1, small if approximately 0.2, medium if roughly 0.5, large if about 0.8, very large if near 1.2, and huge if approaching 2 (Sawilowsky, 2009 ).

The heterogeneity of estimates was assessed by the researchers using I 2 , Q , z , and p values. The degree of heterogeneity was categorized as unimportant if I 2 was <40%, moderate if I 2 was between 30% and 60%, substantial if I 2 was between 50% and 90%, and considerable if it ranged from 75% to 100% (Higgins and Green, 2021 ). We employed a random-effect model for meta-analysis if I 2 was >50%, and a fixed-effect model if I 2 was <50%. In addition to I 2 , Q , z , and p values were also considered in determining the level of heterogeneity.

In cases where a single study produced multiple results, we utilized the Statistics Toolkit (STATTOOLS) to merge participant numbers, means, and standard deviations into a single group (Altman et al., 2000 ). We combined various subgroups such as attitudes (Alqarni, 2021 ; Fidana and Tuncel, 2019 ; Sahin and Yilmaz, 2020 ), attractiveness (Albrecht et al., 2013 ), learning interest (Chin and Wang, 2021 ), satisfaction (Huang et al., 2021 ; Ucar et al., 2017 ; Wu et al., 2018 ), and self-efficacy (Lee and Hsu, 2021 ) under the “attitudes” category. The “learning achievements” subgroup included test scores (e.g. Gonzalez et al., 2020 ), academic achievement, academic averages (Selek and Kiymaz, 2020 ), evaluation scores (Gargrish et al., 2021 ), final exam scores (Gonzalez et al., 2020 ), grades of work, financial knowledge (Candra Sari et al., 2021 ), learning outcomes (Stojanović et al., 2020 ), learning performance (Hanafi et al., 2016 ), the mathematical calculation (Ruiz-Ariza et al., 2018 ), operational effectiveness (Mao and Chen, 2021 ), spatial perception skills (Carbonell Carrera and Bermejo Asensio, 2017 ), test and quiz scores (Christopoulos et al., 2021 ), visualization skills (Omar et al., 2019 ), and writing skills (Wang, 2017a ). The “motivation” subgroup focused on learning motivation (Chang et al., 2016 ; Chu et al., 2019 ; Gómez-García et al., 2021 ; Lee and Hsu, 2021 ; Christopoulos et al., 2021 ). The included studies utilized AR technologies in education as the treatment.

If multiple experimental groups were used, preference would be given to the group that was most closely associated with the use of augmented reality (AR). Among the experimental groups that utilized AR, priority would be given to the group that had the most stringent design and provided the most compelling results. When selecting a control group, the one that could provide the most informative comparative results with the experimental group would be selected. In studies where pre- and post-tests were conducted to compare control and experimental groups, data from the post-tests that underwent the treatment would be retrieved.

The sample size, methodological quality, and age of participants can all contribute to the variability of effects observed in a meta-analysis. Larger sample sizes generally lead to more precise estimates of effect size with less variance. Small samples may have greater variability due to sampling error. Studies that are well-designed and implemented with appropriate controls tend to produce more reliable and valid results. Poorly designed studies with bias or confounding factors can produce less trustworthy outcomes and introduce heterogeneity in the meta-analysis. Studies that include participants from different age groups may lead to variations in treatment effects. For example, an intervention may work better for younger individuals but not as well for older populations. Therefore, in this meta-analysis, differences in sample size, methodological quality, and age of participants across studies may have negatively influenced the generalizability of the results.

Testing for hypotheses

H1. The attitudes of learners towards AR-assisted education are significantly more positive compared to those without the aid of AR technologies .

In a random-effect model, the variance is assumed to consist of two components: within-group variation and between-group variation. The group-specific effects are considered random variables that follow a normal distribution with a mean zero and a certain variance. In contrast, a fixed-effect model assumes that each group has its own fixed effect, which is not normally distributed. The interpretation of results from a random-effect model is usually more generalizable than from a fixed-effect model since it accounts for both within-group and between-group variation. However, a random-effect model may have less statistical power than a fixed-effect model when there are only a few groups or when the within-group variability is small. Therefore, the choice between the two models depends on the research question and the specific data characteristics.

The effect model used for conducting the meta-analysis was determined based on the level of heterogeneity. The observed variances in study outcomes across studies were attributed to heterogeneity rather than random errors, specifically in relation to attitudes towards AR-assisted education (indicated by Q  = 171.78, I 2  = 94.2, p  < 0.01 in Table 2 ). As a result, random-effect models were employed to analyze attitudes within the context of AR-assisted education using meta-analytic techniques.

A forest plot was generated using Stata/MP 14.0 software to test the alternative hypotheses (Fig. 2 ). The plot included 11 effect sizes, with individual studies represented by dots in the middle column and the horizontal line indicating 95% confidence intervals. The no-effect line was represented by the middle line, while the diamond at the bottom indicated the pooled result. If the horizontal line or diamond crossed the no-effect line, it suggested non-significant differences. The diamond was located to the right of the middle line, indicating a significantly more favorable attitude in the experimental group compared to the control ( d  = 1.08, 95% CI = 0.44–1.72, z  = 3.32, p  = 0.001 in Table 2 ).

A forest plot of differences in attitudes between control and experimental groups.

To test for publication bias, a funnel plot was created using the same software. Figure 3 shows symmetrically distributed dots along both sides of the middle line, suggesting the absence of publication bias ( z  = 1.63, p  = 0.102 through Begg’s test in Table 3 ). Therefore, researchers accept the first alternative hypotheses.

A funnel plot of publication bias in attitudes.

H2. Learning achievements in AR-assisted education exhibit significantly higher results compared to those achieved through non-AR-assisted education .

In terms of learning achievements, the estimations yielded significant heterogeneity ( Q  = 281.66, p  < 0.01, I 2  = 92.5 in Table 2 ), prompting the researchers to employ a random-effect model for the meta-analysis. The results indicated a significant difference between the experimental and control groups, with the former achieving significantly higher learning outcomes ( d  = 0.85, 95% CI = 0.47–1.22, z  = 4.37, p  < 0.01 in Table 2 and Fig. 4 ). Additionally, there was no indication of publication bias in the data according to the funnel plot analysis (Fig. 5 ) and Begg’s test ( z  = 1.75, p  = 0.08 in Table 3 ), thus leading the researchers to accept the second alternative hypothesis.

A forest plot of differences in learning achievements between control and experimental groups.

A funnel plot of publication bias in learning achievements.

H3. Learning motivation in AR-assisted education shows a substantial increase compared to non-AR-assisted education .

In order to test the alternate hypothesis, researchers utilized a random-effects model for conducting meta-analysis due to significant heterogeneity in estimates ( Q  = 12.52, p  = 0.028, I 2  = 60.1). A forest plot (Fig. 6 ) was created which showed that the pooled estimate of motivation, represented by the diamond, intersected with the no-effect line, indicating no significant difference in motivation between the two groups ( d  = 0.85, 95% CI = 0.47–1.22, z  = 4.37, p  < 0.01 in Table 2 and Fig. 6 ). Additionally, results from Begg’s test ( z  = 1.13, p  = 0.26) and Egger’s test ( z  = 1.18, p  = 0.302 in Table 3 ) depicted symmetric distribution of dots on either side of the middle line in Fig. 7 , thereby indicating no presence of publication bias. Consequently, the third alternative hypothesis was rejected by the researchers.

A forest plot of differences in motivation between control and experimental groups.

A funnel plot of publication bias in motivation.

In order to verify the reliability of our estimate results, we performed sensitivity analyses using the Stata/MP 14.0 program by entering the command “metaninf N M SD N0 M0 SD0, random cohen”. The results are presented in Fig. 8 , where each dot represents an individual study, while the middle line displays the effect size and the lines on both sides represent the upper and lower confidence interval limits. All of the dots fall within the given confidence interval limits when a particular study is excluded. We conducted separate sensitivity analyses for attitudes, learning achievements, and motivation, and obtained the same results, indicating that the overall and separate estimates of our study are reliable and robust. The final results are summarized in Table 4 .

Results of the sensitivity analysis.

Attitudes toward AR for educational purposes

It can be concluded that students exhibit more favorable attitudes towards AR-assisted education than traditional education. Implementing AR technologies in education has the potential to generate excitement and interest among learners, leading to positive attitudes toward AR-assisted learning. This is especially true for those who experience AR technologies for the first time, as they may find the technology curious and even magical (Sahin and Yilmaz, 2020 ; Akram et al., 2021 ). AR technologies have three dimensions that provide students with a more tangible and authentic learning experience, ultimately enhancing learning effectiveness (Wojciechowski and Cellary, 2013 ). AR technologies capture students’ attention, increase their engagement, and immerse them in educational activities, leading to positive attitudes toward AR-assisted education (Perez-Lopez and Contero, 2013 ). Positive attitudes towards AR-assisted education are closely linked to learning achievements in AR contexts (Sahin and Yilmaz, 2020 ). This positive correlation may further reinforce positive attitudes as students’ learning achievements significantly improve when compared to those achieved through traditional learning.

It is reasonable to expect that AR-assisted education can result in significantly higher learning achievements compared to traditional education. The multi-dimensional scaffolding functions of AR technologies may offer novel experiences and stimulate students to participate in the learning process, thereby enhancing their learning achievements (Gilliam et al., 2017 ). AR-assisted learning may also foster students’ curiosity, which can increase their cognitive effort and improve their learning achievements (Kuhn and Lukowicz, 2016 ). Strong curiosity may help students focus on learning content and reduce distractions, leading to improved learning outcomes. In AR-assisted contexts, students typically experience lower cognitive loads than those without the use of AR technologies and also report higher levels of satisfaction (Wu et al., 2018 ). This may further contribute to improved learning achievements facilitated by AR technologies.

Although this study did not find a significant difference in motivation levels between AR-assisted education and traditional methods, it is reasonable to expect such a difference based on the potential benefits of AR technologies. The remarkable functions of AR technologies may encourage students to engage in simulated learning activities and associate virtual with real learning environments (Abdullah, 2022 ), leading to increased learning motivation and the development of positive attitudes towards learning (Tian et al., 2014 ). Students tend to enjoy using AR technologies in their learning, finding them easy and convenient to use, and they report high satisfaction with their AR-assisted learning experiences (Ozarslan, 2013 ), which can reduce their learning anxiety compared to traditional learning (Tomi and Rambli, 2013 ; Al-Ansi, 2021 ). Thus, students are motivated to continue using AR technologies to enhance their learning experiences. Lee and Hsu’s ( 2021 ) failure to detect significant differences in motivation levels might be due to the short duration of their experiment, poor Internet connection, or the use of small smartphones that could hinder students’ ability to effectively utilize AR technologies.

Major findings

The results of this study are in line with previous research (e.g. Christopoulos et al., 2021 ; Carbonell Carrera and Bermejo Asensio, 2017 ), indicating that AR-assisted education generates more positive attitudes among learners and leads to higher learning achievements compared to traditional methods. However, the study did not observe any significant differences in motivation levels between AR-assisted education and non-AR-assisted education. The study authors explored several explanations for this unexpected finding.

Limitations

This study has several limitations. Firstly, due to constraints in the availability of library resources, it was not possible to access all relevant literature. Secondly, Begg’s and Egger’s tests indicate that publication bias exists regarding learning achievements in AR-assisted education, which may reduce the reliability of the findings. Additionally, the variability of research contexts makes it challenging to fully summarize the effects of AR technologies on educational outcomes.

Future research directions

Other factors, such as learning styles and learner personality, may also significantly impact the effects of AR technologies on educational outcomes. Future research could incorporate a more comprehensive range of influencing factors. Additionally, future studies could explore the differences between the application of mobile and static AR technologies in educational contexts (Lee and Hsu, 2021 ). Researchers should also consider the impact of technostress, interaction, affection, cognition, and telepresence on AR-assisted learning experiences and achievements (Baabdullah et al., 2022 ). Furthermore, studies could focus on the effects of AR on learners’ spatial ability (Di and Zheng, 2022 ).

Data availability

The datasets generated during and/or analyzed during the current study are openly at https://osf.io/jfwb2/?view_only=872843fa65cf4d35b35afb7214b793b9 .

Abdullah MAA (2022) Investigating characteristics of learning environments during the COVID-19 pandemic: a systematic review. Can J Learn Technol 48(1):1–27

Google Scholar  

Akçayır M, Akçayır G (2017) Advantages and challenges associated with augmented reality for education: a systematic review of the literature. Educ Res Rev 20:1–11. https://doi.org/10.1016/j.edurev.2016.11.002

Article   Google Scholar  

Akçayır M, Akçayır G, Pektaş HM, Ocak MA (2016) Augmented reality in science laboratories: the effects of augmented reality on university students’ laboratory skills and attitudes toward science laboratories. Comput Hum Behav 57:334–342. https://doi.org/10.1016/j.chb.2015.12.054

Akram H, Yingxiu Y, Al-Adwan AS, Alkhalifah A (2021) Technology integration in higher education during COVID-19: an assessment of online teaching competencies through technological pedagogical content knowledge model. Front Psychol 12:736522. https://doi.org/10.3389/fpsyg.2021.736522

Article   PubMed   PubMed Central   Google Scholar  

Al-Ansi AM (2021) Students anxiety and recruitment during Covid-19 pandemic: role of university, specialization and employment expectation. Perspect Sci Educ 49(1):403–413. https://doi.org/10.32744/pse.2021.1.27

Albrecht UV, Folta-Schoofs K, Behrends M, Von Jan U (2013) Effects of mobile augmented reality learning compared to textbook learning on medical students: randomized controlled pilot study. J Med Internet Res 15(8):e182. https://doi.org/10.2196/jmir.2497

Alqarni T (2021) Comparison of augmented reality and conventional teaching on special needs students’ attitudes towards science and their learning outcomes. J Balt Sci Educ 20(4):558–572. https://doi.org/10.33225/jbse/21.20.558

Altman DG, Machin D, Bryant TN, Gardner MJ (2000) Statistics with confidence, 2nd edn. BMJ Books, pp. 28–31

Baabdullah AM, Alsulaimani AA, Allamnakhrah A, Alalwan AA, Dwivedi YK, Rana NP (2022) Usage of augmented reality (AR) and development of e-learning outcomes: an empirical evaluation of students’ e-learning experience. Comput Educ 177. https://doi.org/10.1016/j.compedu.2021.104383

Basoglu N, Goken M, Dabic M, Ozdemir Gungor D, Daim TU (2018) Exploring adoption of augmented reality smart glasses: applications in the medical industry. Front Eng Manag 5(2):167–181. https://doi.org/10.15302/j-fem-2018056

Begg CB, Mazumdar M (1994) Operating characteristics of a rank correlation test for publication bias. Biometrics 50(4):1088–1101

Article   CAS   PubMed   MATH   Google Scholar  

Bölek KA, De Jong G, Henssen D (2021) The effectiveness of the use of augmented reality in anatomy education: a systematic review and meta-analysis. Sci Rep 11(1). https://doi.org/10.1038/s41598-021-94721-4

Bork F, Lehner A, Eck U, Navab N, Waschke J, Kugelmann D (2020) The effectiveness of collaborative augmented reality in gross anatomy teaching: a quantitative and qualitative pilot study. Anat Sci Educ. https://doi.org/10.1002/ase.2016

Bursali H, Yilmaz RM (2019) Effect of augmented reality applications on secondary school students’ reading comprehension and learning permanency. Comput Hum Behav 95:126–135. https://doi.org/10.1016/j.chb.2019.01.035

Cai S, Liu C, Wang T, Liu E, Liang J (2021) Effects of learning physics using Augmented Reality on students’ self-efficacy and conceptions of learning. Br J Educ Technol 52(1):235–251. https://doi.org/10.1111/bjet.13020

Candra Sari R, Rika Fatimah PL, Ilyana S, Dwi Hermawan H (2021) Augmented reality (AR)-based sharia financial literacy system (AR-SFLS): a new approach to virtual sharia financial socialization for young learners. Int J Islam Middle East Finance Manag. https://doi.org/10.1108/IMEFM-11-2019-0484

Cao X, Hsu Y (2022) Systematic review and meta-analysis of the impact of virtual experiments on students’ learning effectiveness. Interact Learn Environ. https://doi.org/10.1080/10494820.2022.2072898

Carbonell Carrera C, Bermejo Asensio LA (2017) Landscape interpretation with augmented reality and maps to improve spatial orientation skill. J Geogr High Educ 41(1):119–133. https://doi.org/10.1080/03098265.2016.1260530

Cavallo AM, Laubach TA (2001) Students’ science perceptions and enrollment decisions in differing learning cycle classrooms. J Res Sci Teach 38(9):1029–1062. https://doi.org/10.1002/tea.1046

Cen L, Ruta D, Mohd LM, Ng J (2020) Augmented Immersive Reality (AIR) for improved learning performance: a quantitative evaluation. IEEE Trans Learn Technol 13:283–296. https://doi.org/10.1109/TLT.2019.2937525

Chang RC, Chung LY, Huang YM (2016) Developing an interactive augmented reality system as a complement to plant education and comparing its effectiveness with video learning. Interact Learn Environ 24(6):1245–1264. https://doi.org/10.1080/10494820.2014.982131

Chin KY, Wang CS (2021) Effects of augmented reality technology in a mobile touring system on university students’ learning performance and interest. Australas J Educ Technol 37(1):27–42. https://doi.org/10.14742/ajet.5841

Christopoulos A, Pellas N, Kurczaba J, Macredie R (2021) The effects of augmented reality‐supported instruction in tertiary‐level medical education. Br J Educ Technol. https://doi.org/10.1111/bjet.13167

Chu HC (2014) Potential negative effects of mobile learning on students’ learning achievement and cognitive load—a format assessment perspective. J Educ Technol Soc 17(1):332–344. http://www.jstor.org/stable/jeductechsoci.17.1.332

Chu HC, Chen JM, Hwang GJ, Chen TW (2019) Effects of formative assessment in an augmented reality approach to conducting ubiquitous learning activities for architecture courses. Univers Access Inf Soc 18(2):221–230. https://doi.org/10.1007/s10209-017-0588-y

Ciloglu T, Ustun AB (2023) The effects of mobile AR-based biology learning experience on students’ motivation, self‐efficacy, and attitudes in online learning. J Sci Educ Technol. https://doi.org/10.1007/s10956-023-10030-7

Delello JA (2014) Insights from pre-service teachers using science-based augmented reality. J Comput Educ 1(4):295–311. https://doi.org/10.1007/s40692-014-0021-y

Demitriadou E, Stavroulia KE, Lanitis A (2020) Comparative evaluation of virtual and augmented reality for teaching mathematics in primary education. Educ Inf Technol 25:381–401. https://doi.org/10.1007/s10639-019-09973-5

Di X, Zheng X (2022) A meta-analysis of the impact of virtual technologies on students’ spatial ability. Educ Technol Res Dev. https://doi.org/10.1007/s11423-022-10082-3

Egger M, Smith GD, Schneider M, Minder C (1997) Bias in meta-analysis detected by a simple, graphical test. Br Med J 315:629–634. https://www.jstor.org/stable/25175671

Article   CAS   Google Scholar  

Fidana M, Tuncel M (2019) Integrating augmented reality into problem based learning: the effects on learning achievement and attitude in physics education. Comput Educ 142:103635. https://doi.org/10.1016/j.compedu.2019.103635

Gargrish S, Kaur DP, Mantri A, Singh G, Sharma B (2021) Measuring effectiveness of augmented reality-based geometry learning assistant on memory retention abilities of the students in 3D geometry. Comput Appl Eng Educ. https://doi.org/10.1002/cae.22424

Garzón J, Acevedo J (2019) Meta-analysis of the impact of Augmented Reality on students’ learning gains. Educ Res Rev 27:244–260. https://doi.org/10.1016/j.edurev.2019.04.001

Garzón J, Kinshuk, Baldiris S, Gutiérrez J, Pavón J (2020) How do pedagogical approaches affect the impact of augmented reality on education? A meta-analysis and research synthesis. Educ Res Rev 31:100334. https://doi.org/10.1016/j.edurev.2020.100334

Garzón J, Pavón J, Baldiris S (2019) Systematic review and meta-analysis of augmented reality in educational settings. Virtual Real 23(4):447–459. https://doi.org/10.1007/s10055-019-00379-9

Gilliam M, Jagoda P, Fabiyi C, Lyman P, Wilson C, Hill B, Bouris A (2017) Alternate reality games as an informal learning tool for generating STEM engagement among underrepresented youth: a qualitative evaluation of the source. J Sci Educ Technol 26(3):295–308. https://doi.org/10.1007/s10956-016-9679-4

Gómez-García G, Hinojo-Lucena FJ, Alonso-García S, Romero-Rodríguez JM (2021) Mobile learning in pre-service teacher education: perceived usefulness of AR technology in primary education. Educ Sci 11(6):275. https://doi.org/10.3390/educsci11060275

Gonzalez AA, Lizana PA, Pino S, Miller BG, Merino C (2020) Augmented reality-based learning for the comprehension of cardiac physiology in undergraduate biomedical students. Adv Physiol Educ 44(3):314–322. https://doi.org/10.1152/advan.00137.2019

Article   PubMed   Google Scholar  

Hanafi HFB, Said CS, Ariffin AH, Zainuddin NA, Samsuddin K (2016) Using a collaborative Mobile Augmented Reality learning application (CoMARLA) to improve Improve Student Learning. IOP Conf Ser 160:012111. https://doi.org/10.1088/1757-899x/160/1/012111

Higgins JPT, Green S (2021) Cochrane handbook for systematic reviews of interventions version 5.1.0 [updated March 2011]. The Cochrane Collaboration 2011. https://handbook-5-1.cochrane.org/

Hou HT, Fang YS, Tang JT (2021) Designing an alternate reality board game with augmented reality and multi-dimensional scaffolding for promoting spatial and logical ability. Interact Learn Environ 1–21. https://doi.org/10.1080/10494820.2021.1961810

Huang HM, Huang TC, Cheng CY (2021) Reality matters? exploring a tangible user interface for augmented-reality-based fire education. Univers Access Inf Soc. https://doi.org/10.1007/s10209-021-00808-0

Hwang GJ, Wu PH, Chen CC, Tu NT (2016) Effects of an augmented reality-based educational game on students’ learning achievements and attitudes in real-world observations. Interact Learn Environ 24(8):1895–1906. https://doi.org/10.1080/10494820.2015.1057747

Kuhn J, Lukowicz P (2016) gPhysics—using smart glasses for head-centered, context-aware learning in physics experiments. IEEE Trans Learn Technol 9(4):304–317. https://doi.org/10.1109/TLT.2016.2554115

Lee CJ, Hsu Y (2021) Sustainable education using augmented reality in vocational certification courses. Sustainability 13(11):6434. https://doi.org/10.3390/su13116434

Liu YC, Huang TH, Lin IH (2022) Hands-on operation with a Rolling Alphabet-AR System improves English learning achievement. Innov Language Learn Teach. https://doi.org/10.1080/17501229.2022.2153852

Mao CC, Chen CH (2021) Augmented reality of 3D content application in common operational picture training system for army. Int J Hum–Comput Interact 1–17. https://doi.org/10.1080/10447318.2021.1917865

Najmi AH, Alhalafawy WS, Zaki MZT (2023) Developing a sustainable environment based on augmented reality to educate adolescents about the dangers of electronic gaming addiction. Sustainability 15(4):3185. https://doi.org/10.3390/su15043185

Omar M, Ali DF, Mokhtar M, Zaid NM, Jambari H, Ibrahim NH (2019) Effects of mobile augmented reality (MAR) towards students’ visualization skills when learning orthographic projection. Int J Emerg Technol Learn 14(20):106. https://doi.org/10.3991/ijet.v14i20.11463

Ozarslan Y (2013) Effects of the effect of learning materials that are enriched through extended reality on student’s achievement and satisfaction. Doctoral Thesis. Anadolu University Social Sciences Institute, Eskis¸ehir

Ozdemir M, Sahin C, Arcagok S, Demir MK (2018) The effect of augmented reality applications in the learning process: a meta-analysis study. Eurasian J Educ Res 74:165–186. https://doi.org/10.14689/ejer.2018.74.9

Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD et al. (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. J Clin Epidemiol 75(2):192–192. https://doi.org/10.1016/j.rec.2021.10.019

Perez-Lopez D, Contero M (2013) Delivering educational multimedia contents through an augmented reality application: a case study on its impact on knowledge acquisition and retention. Turk Online J Educ Technol 12(4):19–28. https://doi.org/10.2307/3426023

Ruiz-Ariza A, Casuso RA, Suarez-Manzano S, Martínez-López EJ (2018) Effect of augmented reality game Pokémon GO on cognitive performance and emotional intelligence in adolescent young. Comput Educ 116:49–63. https://doi.org/10.1016/j.compedu.2017.09.002

Sahin D, Yilmaz RM (2020) The effect of Augmented Reality Technology on middle school students’ achievements and attitudes towards science education. Comput Educ 144:103710. https://doi.org/10.1016/j.compedu.2019.103710

Saleem M, Kamarudin S, Shoaib HM, Nasar A (2021) Influence of augmented reality app on intention towards e-learning amidst COVID-19 pandemic. Interact Learn Environ. https://doi.org/10.1080/10494820.2021.1919147

Sawilowsky SS (2009) New effect size rules of thumb. J Modern Appl Stat Methods 8(2):597–599. https://doi.org/10.22237/jmasm/1257035100

Article   MathSciNet   Google Scholar  

Sedgwick P, Marston L (2013) Meta-analyses: standardised mean differences. Br Med J 347:f7257. https://doi.org/10.1136/bmj.f7257

Selek M, Kiymaz YE (2020) Implementation of the augmented reality to electronic practice. Comput Appl Eng Educ 28(2):420–434. https://doi.org/10.1002/cae.22204

Stojanović D, Bogdanović Z, Petrović L, Mitrović S, Labus A (2020) Empowering learning process in secondary education using pervasive technologies. Interact Learn Environ 1–14. https://doi.org/10.1080/10494820.2020.1806886

Tezer M, Yıldız EP, Masalimova ARR, Fatkhutdinova AM, Zheltukhina MRR, Khairullina ER(2019)Trends of augmented reality applications and research throughout the world: meta-analysis of theses, articles and papers between 2001–2019 years Int J Emerg Technol Learn 14(22):154. https://doi.org/10.3991/ijet.v14i22.11768

Tian K, Endo M, Urata M, Mouri K, Yasuda T (2014) Multi-viewpoint smartphone AR-based learning system for astronomical observation. Int J Comput Theory Eng 6(5):396–400. https://doi.org/10.3991/ijim.v8i3.3731

Tomi AB, Rambli DRA (2013) An interactive mobile augmented reality magical playbook: learning number with the thirsty crow. Procedia Comput Sci 25:123–130. https://doi.org/10.1016/j.procs.2013.11.015

Ucar E, Ustunel H, Civelek T, Umut I (2017) Effects of using a force feedback haptic augmented simulation on the attitudes of the gifted students towards studying chemical bonds in virtual reality environment. Behav Inf Technol 36(5):540–547. https://doi.org/10.1080/0144929x.2016.1264483

Wang YH (2017a) Exploring the effectiveness of integrating augmented reality-based materials to support writing activities. Comput Educ 113:162–176. https://doi.org/10.1016/j.compedu.2017.04.013

Wang YH (2017b) Using augmented reality to support a software editing course for college students. J Comput Assist Learn 33:532–546. https://doi.org/10.1111/jcal.12199

Weng C, Otanga S, Christianto S, Chu R (2020) Enhancing student’s biology learning by using augmented reality as a learning supplement. J Educ Comput Res 58(4):747–770. https://doi.org/10.1177/0735633119884213

Wojciechowski R, Cellary W (2013) Evaluation of learners’ attitude toward learning in ARIES augmented reality environments. Comput Educ 68:570–585. https://doi.org/10.1016/j.compedu.2013.02.014

Wu PH, Hwang GJ, Yang ML, Chen CH (2018) Impacts of integrating the repertory grid into an augmented reality-based learning design on students’ learning achievements, cognitive load and degree of satisfaction. Interact Learn Environ 26(2):221–234. https://doi.org/10.1080/10494820.2017.1294608

Yang FCO, Lai HM, Wang YW (2022) Effect of augmented reality-based virtual educational robotics on programming students’ enjoyment of learning, computational thinking skills, and academic achievement. Comput Educ 195:104721. https://doi.org/10.1016/j.compedu.2022.104721

Yildirim S (2016) The effect of augmented reality applications in Science courses on students’ achievement, motivation, perception towards problem solving skills and attitudes. Non-published master’s thesis, Ankara University Institute of Educational Sciences, Ankara

Yousef AMF (2021) Augmented reality assisted learning achievement, motivation, and creativity for children of low‐grade in primary school. J Comput Assist Learn 37(4):966–977. https://doi.org/10.1111/jcal.12536

Zhang J, Sung YT, Hou HT, Chang KE (2014) The development and evaluation of an augmented reality-based armillary sphere for astronomical observation instruction. Comput Educ 73:178–188. https://doi.org/10.1016/j.compedu.2014.01.003

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Acknowledgements

The authors extend gratitude for funding support from the following: Shan Dong Humanities and Social Sciences Project in 2022 (Grant No: 2022-JCJY-09): A Study on English College Instructors' Leadership in China, funded by Shandong Federation of Social Sciences; 2019 MOOC of Beijing Language and Culture University (MOOC201902) (Important) “Introduction to Linguistics”; “Introduction to Linguistics” of online and offline mixed courses in Beijing Language and Culture University in 2020; Special fund of Beijing Co-construction Project-Research and reform of the “Undergraduate Teaching Reform and Innovation Project” of Beijing higher education in 2020-innovative “multilingual +” excellent talent training system (202010032003); the research project of Graduate Students of Beijing Language and Culture University “Xi Jinping: The Governance of China” (SJTS202108).

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Cao, W., Yu, Z. The impact of augmented reality on student attitudes, motivation, and learning achievements—a meta-analysis (2016–2023). Humanit Soc Sci Commun 10 , 352 (2023). https://doi.org/10.1057/s41599-023-01852-2

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Augmented Reality (AR) aims to modify the perception of real-world images by overlaying digital data on them. A novel mechanic, it is an enlightening and engaging mechanic that constantly strives for new techniques in every sphere. The real world can be augmented with information in real-time. AR aims to accept the outdoors and come up with a novel and efficient model in all application areas. A wide array of fields are displaying real-time computer-generated content, such as education, medicine, robotics, manufacturing, and entertainment. Augmented reality is considered a subtype of mixed reality, and it is treated as a distortion of virtual reality. The article emphasizes the novel digital technology that has emerged after the success of Virtual Reality, which has a wide range of applications in the digital age. There are fundamental requirements to understand AR, such as the nature of technology, architecture, the devices required, types of AR, benefits, limitations, and differences with VR, which are discussed in a very simplified way in this article. As well as a year-by-year tabular overview of the research papers that have been published in the journal on augmented reality-based applications, this article aims to provide a comprehensive overview of augmented reality-based applications. It is hard to find a field that does not make use of the amazing features of AR. This article concludes with a discussion, conclusion, and future directions for AR.

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Data Availability

Azuma R, Baillot Y, Behringer R, Feiner S, Julier S, MacIntyre B (2001) Recent advances in Augmented Reality. IEEE Comput Graph Appl 21(6):34–47. https://doi.org/10.1109/38.963459

Article   Google Scholar  

Zhang Z, Weng D, Jiang H, Liu Y, Wang Y (2018) Inverse augmented reality: a virtual agent’s perspective. In: IEEE international symposium on mixed and augmented reality adjunct (ISMAR-Adjunct). pp 154–157

Poushneh A (2018) Augmented reality in retail: a trade-off between user’s control of access to personal information and augmentation quality. J Retail Consum Serv 41:169–176. https://doi.org/10.1016/j.jretconser.2017.12.010

Clark A, Dünser A (2012) An interactive augmented reality coloring book. In: 2012 IEEE Symposium on 3D User Interfaces (3DUI), pp 7–10. https://doi.org/10.1109/3DUI.2012.6184168

Cong W (2013) Links and differences between augmented reality and virtual reality. Break Technol 5:57–61

Google Scholar  

Lu Y, Smith S (2007) Augmented reality E-commerce assistant system: trying while shopping. In: Jacko JA (ed) Human–computer interaction. Interaction platforms and techniques. HCI 2007. Lecture notes in computer science, vol 4551. Springer, Berlin

Wojciechowski R, Walczak K, White and Cellary W (2004) Building virtual and augmented reality museum exhibitions. In: Proceedings of the ninth international conference on 3D web technology—Web3D ’04. pp 135–145

Ong SK, Yuan ML, Nee AYC (2008) Augmented reality applications in manufacturing: a survey. Int J Prod Res 46(10):2707–2742

Article   MATH   Google Scholar  

Zollmann S, Hoppe C, Kluckner S, Poglitsch C, Bischof H, Reitmayr G (2014) Augmented reality for construction site monitoring and documentation. Proc IEEE 102(2):137–154. https://doi.org/10.1109/JPROC.2013.2294314

Patil S, Prabhu C, Neogi O, Joshi AR, Katre N (2016) E-learning system using augmented reality. In: Proceedings of the international conference on computing communication control and automation (ICCUBEA). pp 1–5

Damiani L, Demartini M, Guizzi G, Revetria R, Tonelli F (2018) Augmented and virtual reality applications in industrial systems: a qualitative review towards the industry 4.0 era. IFAC-PapersOnLine 51(11):624–630. https://doi.org/10.1016/j.ifacol.2018.08.388

Cipresso P, Giglioli IAC, Raya MA, Riva G (2018) The past, present, and future of virtual and augmented reality research: a network and cluster analysis of the literature. Front Psychol 9:1–20

Challenor J, Ma M (2019) A review of augmented reality applications for history education and heritage visualisation. Multimodal Technol Interact 3(2):39. https://doi.org/10.3390/mti3020039

Pascoal R, Alturas B, de Almeida A, Sofia R (2018) A survey of augmented reality: making technology acceptable in outdoor environments. In: Proceedings of the 13th Iberian conference on information systems and technologies (CISTI). pp 1–6

Kim SJJ (2012) A user study trends in augmented reality and virtual reality research: a qualitative study with the past three years of the ISMAR and IEEE VR conference papers. In: International symposium on ubiquitous virtual reality. https://doi.org/10.1109/isuvr.2012.17

Wanga CH, Chianga YC, Wanga MJ (2015) Evaluation of an augmented reality embedded on-line shopping system. In: Proceedings of 6th international conference on applied human factors and ergonomics (AHFE 2015)

Chen Y, Wang Q, Chen H, Song X, Tang H, Tian M (2019) An overview of augmented reality technology. IOP Conf Ser J Phys Conf Ser 1237:022082. https://doi.org/10.1088/1742-6596/1237/2/022082

Kamboj D, Wankui L, Gupta N (2013) A review on illumination techniques in augmented reality. In: Fourth international conference on computing, communications and networking technologies (ICCCNT). pp 1–9

Irshad S, Rohaya B, Awang Rambli D (2014) User experience of mobile augmented reality: a review of studies. In: Proceedings of the 3rd international conference on user science and engineering (i-USEr). pp 125–130

Novak-Marcincin J, Janak M, Barna J, Novakova-Marcincinova L (2014) Application of virtual and augmented reality technology in education of manufacturing engineers. In: Rocha Á, Correia A, Tan F, Stroetmann K (eds) New perspectives in information systems and technologies, Volume 2, vol 276. Springer, Cham

Chapter   Google Scholar  

Mekni M, Lemieux A (2014) Augmented reality: applications, challenges and future trends. Appl Comput Sci 20:205–214

Rosenblum L (2000) Virtual and augmented reality 2020. IEEE Comput Graph Appl 20(1):38–39. https://doi.org/10.1109/38.814551

Cruz E, Orts-Escolano S, Donoso F (2019) An augmented reality application for improving shopping experience in large retail stores. Virtual Reality 23:281–291

Chatzopoulos D, Bermejo C, Huang Z, Hui P (2017) Mobile augmented reality survey: from where we are to where we go. IEEE Access 5:6917–6950

Mehta D, Chugh H, Banerjee P (2018) Applications of augmented reality in emerging health diagnostics: a survey. In: Proceedings of the international conference on automation and computational engineering (ICACE). pp 45–51

Yeh S, Li Y, Zhou C, Chiu P, Chen J (2018) Effects of virtual reality and augmented reality on induced anxiety. IEEE Trans Neural Syst Rehabil Eng 26(7):1345–1352

Umeda R, Seif MA, Higa H, Kuniyoshi Y (2017) A medical training system using augmented reality. In: Proceedings of the international conference on intelligent informatics and biomedical sciences (ICIIBMS). pp 146–149

Chandrasekaran S, Kesavan U (2017) Augmented reality in broadcasting. In: IEEE international conference on consumer electronics-Asia (ICCE-Asia). pp 81–83

Nasser N (2018) Augmented reality in education learning and training. In: Proceedings of the joint international conference on ICT in education and training, international conference on computing in Arabic, and international conference on geocomputing. pp 1–7

Ashfaq Q, Sirshar M (2018) Emerging trends in augmented reality games. In: Proceedings of the international conference on computing, mathematics and engineering technologies (iCoMET). pp 1–7

Aggarwal R, Singhal A (2019) Augmented Reality and its effect on our life. In: Proceedings of the 9th international conference on cloud computing, data science & engineering. pp 510–515

Rana K, Patel B (2019) Augmented reality engine applications: a survey. In: Proceedings of the international conference on communication and signal processing (ICCSP). pp 380–384

He et al (2017) The research and application of the augmented reality technology. In: Proceedings of the 2nd information technology, networking, electronic and automation control conference (ITNEC). pp 496–501

Oyman M, Bal D, Ozer S (2022) Extending the technology acceptance model to explain how perceived augmented reality affects consumers’ perceptions. Comput Hum Behav 128:107127. https://doi.org/10.1016/j.chb.2021.107127

Liu Y, Kumar SV, Manickam A (2022) Augmented reality technology based on school physical education training. Comput Electr Eng 99:107807

Giannopulu B, Lee TJ, Frangos A (2022) Synchronised neural signature of creative mental imagery in reality and augmented reality. Heliyon 8(3):e09017. https://doi.org/10.1016/j.heliyon.2022.e09017

Sun C, Fang Y, Kong M, Chen X, Liu Y (2022) Influence of augmented reality product display on consumers’ product attitudes: a product uncertainty reduction perspective. J Retail Consum Serv 64:102828

Menon SS, Holland C, Farra S, Wischgoll T, Stuber M (2022) Augmented reality in nursing education—a pilot study. Clin Simul Nurs 65:57–61

Pimentel D (2022) Saving species in a snap: on the feasibility and efficacy of augmented reality-based wildlife interactions for conservation. J Nat Conserv 66:126151

Yavuz M, Çorbacloğlu E, Başoğlu AN, Daim TU, Shaygan A (2021) Augmented reality technology adoption: case of a mobile application in Turkey. Technol Soc 66:101598

Bussink T, Maal T, Meulstee J, Xi T (2022) Augmented reality guided condylectomy. Br J Oral Maxillofac Surg 60:991

Mohanty BP, Goswami L (2021) Advancements in augmented reality. Mater Today Proc. https://doi.org/10.1016/j.matpr.2021.03.696

Kolivand H, Mardenli I, Asadianfam S (2021) Review on augmented reality technology. In: Proceedings of 14th international conference on developments in esystems engineering (DeSE). pp 7–12. https://doi.org/10.1109/DeSE54285.2021.9719356

Mishra H, Kumar A, Sharma M, Singh M, Sharma R, Ambikapathy A (2021) Application of augmented reality in the field of virtual labs. international conference on advance computing and innovative technologies in engineering (ICACITE). pp 403–405. https://doi.org/10.1109/ICACITE51222.2021.9404705

Liu Y, Sun Q, Tang Y, Li, Y, W. Jiang W, Wu J (2020) Virtual reality system for industrial training. In: 2020 international conference on virtual reality and visualization (ICVRV). pp 338–339

VanHorn K, Çobanoglu MC (2022) Democratizing AI in biomedical image classification using virtual reality, democratizing AI in biomedical image classification using virtual reality. Virtual Reality 26(1):159–171

Lemmens JS, Simon M, Sumter SR (2022) Fear and loathing in VR: the emotional and physiological effects of immersive games. Virtual Reality 26(1):223–234

Rodríguez G, Fernandez DMR, Pino-Mejías MA (2020) The impact of virtual reality technology on tourists’ experience: a textual data analysis. Soft Comput 24:13879–13892. https://doi.org/10.1007/s00500-020-04883-y

Gong M (2021) Analysis of architectural decoration esthetics based on VR technology and machine vision. Soft Comput 25:12477–12489

Lu W, Zhao L, Xu R (2021) Remote sensing image processing technology based on mobile augmented reality technology in surveying and mapping engineering. Soft Comput. https://doi.org/10.1007/s00500-021-05650-3

Lorenz M, Knopp S, Klimant P (2018) Industrial augmented reality: requirements for an augmented reality maintenance worker support system. In: IEEE international symposium on mixed and augmented reality adjunct (ISMAR-Adjunct). pp 151–153. https://doi.org/10.1109/ISMAR-Adjunct.2018.00055

Kim J, Lorenz M, S. Knopp S, Klimant P (2020) Industrial augmented reality: concepts and user interface designs for augmented reality maintenance worker support systems. In: IEEE International symposium on mixed and augmented reality adjunct (ISMAR-Adjunct). pp 67–69. https://doi.org/10.1109/ISMAR-Adjunct51615.2020.00032

Kim J, Lorenz M, Knopp S and Klimant P (2020) Industrial augmented reality: concepts and user interface designs for augmented reality maintenance worker support systems. In: IEEE international symposium on mixed and augmented reality adjunct (ISMAR-Adjunct). pp. 67–69. https://doi.org/10.1109/ISMAR-Adjunct51615.2020.00032

De Souza RF, Farias DL, Flor da Rosa RCL, Damasceno EF (2019) Analysis of low-cost virtual and augmented reality technology in case of motor rehabilitation. In: Proceedings of 21st symposium on virtual and augmented reality (SVR). pp 161–164. https://doi.org/10.1109/SVR.2019.00039

Ping J, Liu Y, Weng D (2019) Comparison in depth perception between virtual reality and augmented reality systems. In: IEEE conference on virtual reality and 3D user interfaces (VR). pp 1124–1125. https://doi.org/10.1109/VR.2019.8798174

Phon DNE, Ali MB, Halim NDA (2014) Collaborative augmented reality in education: a review. In: International conference on teaching and learning in computing and engineering. pp 78–83

Tatwany L, Ouertani HC (2017) A review on using augmented reality in text translation. In: Proceedings of 6th international conference on information and communication technology and accessibility (ICTA). pp 1–6. https://doi.org/10.1109/ICTA.2017.8336044

Kurniawan C, Rosmansyah Y, Dabarsyah B (2019) A systematic literature review on virtual reality for learning. In: Proceedings of the 5th international conference on wireless and telematics (ICWT). pp 1–4

Chen J, Yang J (2009) Study of the art & design based on Virtual Reality. In: Proceedings of the 2nd IEEE international conference on computer science and information technology. pp 1–4

Zhang Y, Liu H, Kang S-C, Al-Hussein M (2020) Virtual reality applications for the built environment: research trends and opportunities. Autom Constr 118:1–19. https://doi.org/10.1016/j.autcon.2020.103311

Boud AC, Haniff DJ, Baber C and Steiner SJ (1999) Virtual reality and augmented reality as a training tool for assembly tasks. In: Proceedings of the IEEE international conference on information visualization. https://doi.org/10.1109/iv.1999.781532

Khan T, Johnston K, Ophoff J (2019) The impact of an augmented reality application on learning motivation of students. Adv Hum-Comput Interact 2019:1–14

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Dargan, S., Bansal, S., Kumar, M. et al. Augmented Reality: A Comprehensive Review. Arch Computat Methods Eng 30 , 1057–1080 (2023). https://doi.org/10.1007/s11831-022-09831-7

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Systematic review article, consumer behavior in augmented shopping reality. a review, synthesis, and research agenda.

• 1 Department of marketing, University of Kiel, Kiel, Germany
• 2 Grenoble École de Management, Grenoble, France

The application of augmented reality (AR) is receiving great interest in e-commerce, m-commerce, and brick-and-mortar-retailing. A growing body of literature has explored several different facets of how consumers react to the upcoming augmented shopping reality. This systematic literature review summarizes the findings of 56 empirical papers that analyzed consumers’ experience with AR, acceptance of AR, and behavioral reactions to AR in various online and offline environments. The review synthesizes current knowledge and critically discusses the empirical studies conceptually and methodologically. Finally, the review outlines the theoretical basis as well as the independent, mediating, moderating, and dependent variables analyzed in previous AR research. Based on this synthesis, the paper develops an integrative framework model, which helps derive directives for future research on augmented shopping reality.

Introduction

The augmented reality (AR) technology supplements the real world with virtual elements. These supplements are often visual like in the mobile game Pokémon Go, where the digital Pokémons extend the physical environment ( Hamari et al., 2019 ), but they could also address other senses like hearing, for example through interactive audio AR in participatory performance ( Nagele et al., 2021 ), smelling in synesthetic visualization of odors with an odor detector (e-nose; Ward et al., 2020 ) or tasting by a pseudo-gustatory display ( Narumi et al., 2011 ). Several reports have recently ranked AR as one of the top 10 technology trends ( Marr, 2020 ; Samsung Business Insights, 2020 ). In a similar vein, the report of Euromonitor International describes “phygital reality” as a top 10 global consumer trend in 2021 ( Westbrook and Angus, 2021 ). Phygital reality is understood as a hybrid bridging the physical and digital world regarding various aspects of human behavior, including living, working, and shopping. According to this report, half of the consumers younger than 45 years have used augmented reality and virtual reality in 2020 ( Westbrook and Angus, 2021 ). Evidently, consumers are increasingly used to integrate this technology into their lives. For example, the Covid-19 pandemic caused lockdowns in 2020 and 2021, calling for social distancing in many countries. Technologies like video conferencing rapidly became widespread, shifting personal and business contacts to virtual rooms. With such developments boosting people’s view on technology ( Hacker et al., 2020 ) and the fast diffusion of devices that principally enable AR-based applications, the relevance of the AR technology will continue to grow. This is particularly true as these mobile devices are often considered “constant companions” (e.g., smartphones or tablet computers). Accordingly, the AR market is expected to reach a US $75 billion in revenue by 2023 ( vXchange, 2020 ) and the global AR and VR market revenue of US $161.1 billion by 2025 ( Vynz Research, 2020 ). In their opinion paper, Dwivedi et al. (2021) , p . 16) recently concluded that augmented reality is still in its infancy, but they forecast that it “will be as prevalent in the marketing of the future as the Internet is today”.

The AR technology has already entered the shopping world. Companies and retailers can feasibly apply AR in e-commerce and m-commerce (e.g., Javornik, 2016b ; Baek et al., 2018 ; Beck and Crié, 2018 ). In these online retailing contexts, AR enables consumers to visualize or even virtually try-on products, such as apparel, eyewear, or cosmetics. AR-enabled virtual try-ons or virtual fitting rooms allow consumers to make better choices. As a positive side effect, this may eventually help decrease the excessive return rates of apparel ordered online ( Narvar, 2017 ). AR can also be helpful in brick-and-mortar retailing ( Hilken et al., 2018 ; Caboni and Hagberg, 2019 ), where the technology can enhance the physical products or shelves with digital information (e.g., van Esch et al., 2019 ; Wedel et al., 2020 ; Joerß et al., 2021 ).

As an umbrella term for AR applications in shopping and retailing environments, we coin the term augmented shopping reality (ASR). However, despite the aforementioned benefits and wide diffusion of AR-enabling devices, the diffusion of ASR is still in an early phase. According to a recent WBR (2020) insights report, only 1 out of 100 retailers is currently using AR. Many companies state that the lack of the ability to currently support these features is the main obstacle. Yet, most of the surveyed managers report that they plan to adopt the technology in the near future. Online sellers and offline retailers require more knowledge about how consumers react to the technology and how to design effective AR applications. For example, based on research insights, ASR could potentially be more effective in addressing different senses when utilizing the crossmodal design paradigm ( Ward et al., 2021 ) which is known to influence decision processes ( Deliza and Macfie, 1996 ) and perceived value ( Teas and Agarwal, 2000 ). As another example, ASR could be more effective making use of the latest research findings on the design of AR information at the point of sale ( Hoffmann et al., 2022 ). Academic ASR research is developing with tremendous speed, but the growing body of literature is very diverse and fragmented in that the extant studies cover different AR applications, shopping settings, and product categories, with each study putting the spotlight on a specific context. Also, the findings are published in different fields, such as business (e.g., Rauschnabel et al., 2018 ; Jessen et al., 2020 ; Smink et al., 2020 ), marketing (e.g., Hilken et al., 2017 ), retailing (e.g., Heller et al., 2019a , b ; Pantano et al., 2017 ; Watson et al., 2020 ), information science (e.g., Huang and Liao, 2015 ; Brito and Stoyanova, 2018 ; McLean and Wilson, 2019 ), and psychology (e.g., Choi and Choi, 2020 ). Consequently, notable voids exist in the literature, among others, regarding whether or not AR taps the same or different ASR functionalities in e-/m-commerce and brick-and-mortar-retailing. Marketers need to know which ASR functionality they can use in which setting, for which product categories, and how they have to design the ASR for different applications. Another void emerges in how the cluttered empirical findings about user experiences, technology acceptance, marketing outcomes, etc. can be integrated into a general customer-centric framework to understand the whole customer journey. To resolve this confusion and provide scholars, managers and ASR designers with a cohesive understanding of the current state-of-art, a systematic integration is needed. To fill these voids, this paper synthesizes the relevant literature’s achievement, develops a new holistic theoretical framework by integrating past empirical findings and enhancing them based on conceptual works, and then outlines future trajectories and research directions.

The paper will answer the following research questions: 1) Are there contingencies between the different ASR functionalities (informing, visualizing, trying-on, and placing) and the context in which they are used, including the retailing channel, product category and AR device? 2) Which theories build the foundation for empirical AR research on consumer behaviors in ASR, and how can these partial explanations be integrated into a sound framework? 3) Which models of consumer behavior have been developed and empirically tested, especially for the different contexts of ASR? 4) Which methodologies have scholars applied, and which research methods are needed in the future given the more mature state of the field? 5) How can the formal functions of the predictor, mediator, and outcome variables of previous AR studies be organized, and which moderators and boundary conditions are relevant for developing one integrative framework model? 6) What are the research gaps in the consumer behavior literature on ASR, and which directions are most relevant for further investigations in this context?

We conduct a systematic literature review to assess the current state-of-the-art of ASR research. The review covers 56 papers, which report empirical studies on consumer behavior in ASR. In particular, we highlight which ASR functions (informing, visualizing, trying-on, and placing) are tested in shopping environments, such as e-commerce, m-commerce, and brick-and-mortar retailing. We systemize and integrate the theoretical basis and conceptual models explored in past research. Footing on this synthesis, the paper develops an integrative framework model that helps derive directions for future research on consumer behavior in ASR. For the first time, we critically review the methodological approaches of past papers and evaluate the research stream from a methodological point of view to provide recommendations for improving the quality of future research. The target audience of this systematic literature review is researchers in marketing, consumer behavior, retailing, media design, and computer science, as well as practitioners in these domains.

Defining augmented shopping reality

Augmented reality.

The AR technology combines real and virtual objects in such a way that they appear to coexist in the same space ( Azuma et al., 2001 ; van Krevelen and Poelman, 2010 ; Skarbez et al., 2021 ). To this end, the technology superimposes digital 3D objects in relation to objects in the analogue world on a screen or any other device display ( Azuma, 1997 ). Furthermore, as a unique property of the AR technology, this augmentation of the real world occurs in real-time ( Azuma, 1997 ; Carmigniani et al., 2011 ), such that users are able to interact with the virtual objects ( Zhou et al., 2008 ). For these reasons, augmentation of the real world with a computer-generated layer and interactivity can be considered the two main features of augmented reality ( Javornik, 2016a ).

The technology, and thus the augmentation of reality, can be achieved on many different types of displays and devices. First, there are fixed interactive screens (e.g., virtual mirrors), computer monitors, and laptops. A second category is portable and handheld devices, such as smartphones, smartwatches, tablet computers, or even optical see-through glasses ( Carmigniani et al., 2011 ; Kim and Hyun, 2016 ; Brito and Stoyanova, 2018 ). Mobile devices are omnipresent nowadays, so they likely boost the diffusion of AR in various settings, opening the technology’s untapped potential. The third category comprises displays of wearable technologies proximal to the user. These include head-mounted displays, such as smart glasses or helmets (e.g., Microsoft HoloLens), which overlay the user’s field of vision with digital objects (e.g., Brito and Stoyanova, 2018 ; Rauschnabel 2018 ; Rauschnabel et al., 2018 ). Finally, in the more distant future, the application of implanted devices, such as lenses, is highly probable ( Flavián et al., 2019 ).

Different fields analyze AR and its practical applications. Research in information technology and computer science explores the technical and functional aspects of the AR technology, such as precise control or exact object positioning ( Zhou et al., 2008 ; Carmigniani et al., 2011 ; Chae et al., 2018 ; Kytö et al., 2018 ). Scholars from different disciplines have also analyzed AR applications through the lenses of their fields, including medicine ( Berryman, 2012 ; Vávra et al., 2017 ), psychology ( Botella et al., 2005 ), education ( Di Serio et al., 2013 ; Bower et al., 2014 ; Harley et al., 2016 ; Chen et al., 2017 ), gaming ( Rauschnabel et al., 2017 ; Hamari et al., 2019 ), or tourism ( Aluri, 2017 ; Chung et al., 2018 ; tom Dieck and Jung, 2018 ). In the business literature, the AR technology has been studied with a focus on production and industry 4.0 ( Masood and Egger, 2019 ; Kaasinen et al., 2020 ) or advertising and branding ( Hopp and Gangadharbatla, 2016 ; Mauroner et al., 2016 ; Yaoyuneyong et al., 2016 ; de Ruyter et al., 2020 ). In this paper, we focus on the applications in retailing environments. Virtual reality (VR) also provides innovative applications for marketing and retailing, and researchers have already analyzed these applications ( Boyd and Koles, 2019 ; Herz and Rauschnabel, 2019 ; Hudson et al., 2019 ; van Berlo et al., 2021 ). However, in contrast to AR, VR creates a complete digital environment where users interact with virtual objects in real-time. AR superimposes computer-generated objects over the real world ( Flavián et al., 2019 ). Therefore, this technology is highly interesting for retailing contexts, such as stationary retailing where AR can provide additional information to physical products or e-tailing where AR can help consumers virtually try-on products. Hence, we focus on AR in this paper.

Augmented shopping reality

AR can be incorporated in retailing settings in several ways, including but not limited to the functionalities of informing, visualizing, trying-on, and placing. We build our typology on prior research that has already suggested classifications of AR functionalities in general (e.g., Azuma, 1997 ) and in retailing contexts. For example, Tan et al. (2021) identified four uses of AR in retailing. However, these categories (entertain, educate customers, evaluate product fit, enhance the postpurchase consumption experience) describe how consumers use the AR technology, while our review will shift the focus on the technological design to disentangle the different functionalities and their uses. Prior research stressed that AR in shopping settings could be used to extend the product, the consumers’ body, and the consumers’ environment ( Javornik 2016a ; Hoffmann et al., 2022 ). Integrating these conceptual foundations, we propose that the AR technology can be used to enhance and support different steps in the customer journey, from searching information over visualizing products to virtually trying on products or virtually placing objects in the consumers’ environment. We accordingly claim that AR provides at least four main groups of functionalities in shopping and retailing settings; we label these ASR functions as informing, visualizing, trying-on, and placing. As a striking advantage of this typology, AR applications can be objectively assigned to the different categories based on their technological design.

The AR technology can be used to enhance physical objects (including products) with virtual information ( Hoffmann et al., 2022 ). This function has been labelled ‘annotation’ by Azuma (1997) and is related to Tan et al.’s (2021) educate category. Tourism agencies use AR to deliver location-based information about sights, or museums provide details about exhibits (CorfuAR; Kourouthanassis et al., 2015 ). Star view apps (Night Sky, Sky View, Star Walk, etc.) are further examples of how AR can deliver context-specific information. In shopping contexts, retailers can apply AR in brick-and-mortar stores to supplement the physical environment with product information ( Hilken et al., 2018 ), such as offering further details about books ( Spreer and Kallweit, 2014 ) or food products ( Joerß et al., 2021 ; Hoffmann et al., 2022 ). Virtual overlays concern the product or specific areas on the packaging and even entire shelves. The conveyed details can be technical details or information about the product origin, ingredients, allergy warnings, etc. As a unique benefit that sets AR systems apart from other traditional means of communication, the AR-enabled information can even provide personalized information ( Hsu et al., 2021 ) without physically altering the product design or its packaging. This aspect is particularly interesting to physical stores because they operate under much stronger space-related constraints in terms of information presentation than do online or mobile shops. In this way, AR opens up virtually unlimited space in the digital world for physical objects at the point of sale. The technology provides shoppers with access to the required information exactly at the place and the time when they need it ( Joerß et al., 2021 ; Hoffmann et al., 2022 ).

Visualizing

The visualization function allows users to see a virtual 3D model of a product or visualize specific aspects of it or certain benefits ( Azuma, 1997 ). Users can interact with the model and turn it to view it from different angles or they might customize the size, colors, and shape. The function has been tested in empirical research, for example, regarding the mobile app of the German car magazine AUTO BILD that can be scanned to experience virtual context ( Rese et al., 2017 ). Other studies tested AR applications to visualize shoes ( Brito and Stoyanova, 2018 ) or mugs ( Huynh et al., 2019 ).

Virtual try-ons allow users to augment themselves with virtual objects. Users of this type of AR application can choose a piece of apparel, shoes, eyewear, cosmetics, or watches and test these products on their own body or their own face in a virtual fitting-room or through a virtual mirror (e.g., Javornik, 2016b ; Hilken et al., 2017 ; Poushneh and Vasquez-Parraga, 2017 ; Yim et al., 2017 ). In particular, sellers of apparel (e.g., Huang and Liao, 2015 ; Baytar et al., 2020 ), eyeglasses and sunglasses (e.g., Ray-Ban Virtual Try-On, Mister Spex), or cosmetics (e.g., Shiseido AR makeup mirror) have developed such virtual try-ons. This AR function is frequently applied in e-commerce and m-commerce to allow consumers try on products in the digital world where testing the product before ordering it is often not feasible or possible. As they help avoid suboptimal decisions, virtual try-ons may be one way to reduce the unreasonably high return rates of non-fitting products. Notably, even brick-and-mortar stores have adapted AR try-on applications, such as virtual mirrors on stationary wide-screen monitors for apparel (e.g., Yuan et al., 2021 ).

The AR function placing (also termed ‘environmental embedding’ by Hilken et al., 2017 or ‘evaluate’ by Tan et al., 2021 ) refers to the augmentation of the physical surrounding of the user with virtual elements. In shopping and retailing contexts, this application is frequently employed for home furniture (e.g., Javornik, 2016b ; Heller et al., 2019a ; Rauschnabel et al., 2019 ). Furniture planners (e.g., IKEA place, Cimagine) invite users to scan or click objects of the catalogue, website or app and place these elements virtually in their physical rooms. Furniture planners support consumers in imagining how these pieces of furniture would look in their rooms. Other applications are paintings ( Mishra et al., 2021 ) or wall paint ( Hilken et al., 2020 ).

Summing up, in shopping contexts, these four AR functionalities differ primarily with regard to the object that is recorded by the device’s camera and which is augmented on the display (the product, a marker, the consumer, or the consumer’s physical surroundings). Secondly, the functionalities differ regarding the attached virtual objects (information, product visualization, and embedded product). Table 1 provides a systematic overview of these differences. Notably, the functions of trying-on and placing both involve a demonstration of the product (like visualizing) but embed the virtual product either in the image of the consumer (trying-on) or the environment (placing). For visualizations of these different functions, we refer readers to some of the empirical articles in our literature review that include pictures of the studied AR technology. For the function ‘informing’, for example, see the shopping apps used by Hoffmann et al. (2022) , Figure 6, 7), Joerß et al. (2021) , p . 520), or Speer and Kallweit. (2014) , p . 22). For the ‘visualizing’ function, consider the CluckAR-app shown by van Esch et al. (2019) , p . 39) or the AUTO BILD ad shown by Rese et al. (2017) , p . 311). Examples for the ‘trying-on’ function are the Ray Ban Website for sunglasses or the Tissot Website for watches shown by Yim et al. (2017) , p . 101). For the function ‘placing’, see the IKEA tool shown by Javornik. (2016b) , p . 97).

TABLE 1 . Applications of AR in retailing.

Arguably, some functionalities may benefit specific product categories (e.g., trying-on for apparel, informing for food). Still, the product categories and functionalities are two distinct aspects that need to be considered separately. For example, AR can add product information to a sweater in a physical store (informing), can visualize the sweater in 3D based on a picture in a catalogue (visualizing), help consumers virtually try on the sweater in e-commerce (trying-on) or the technology can virtually put the sweater in the consumer’s wardrobe (placing).

Search strategy

We conduct a systematic literature review to give an overview of the research in the field of consumer behavior in ASR (see Figure A1 ). We started with a systematic search process following the standard guidelines for systematic reviews ( Moher et al., 2009 ; Palmatier et al., 2018 ; Snyder, 2019 ).

In a first step, we consulted the Web of Science database to search papers using the following terms: (“augmented reality” OR “mixed reality” OR “extended reality”) AND (shopping OR retailing OR e-commerce OR marketing OR consumer OR customer). We allowed only published journal articles. Our initial search resulted in 852 records, which were reduced to 759 when excluding review articles in the search mask (see Figure A1 ).

In a second step, we cleansed the set of papers following pre-defined criteria. We first inspected the title and abstract to include only relevant papers. If necessary, we read the papers to decide whether or not they are appropriate. Selecting only papers with a clear focus on AR, MR, or ER reduced the set of papers to 348. For example, we dropped articles covering the VR technology, but not AR ( Hudson et al., 2019 ). We extracted 84 papers that focus on consumer behavior with regard to AR in retailing to promote products or brands. We excluded studies limited to the technological development, such as comparing different AR technologies. Given our scope on retailing and e-commerce, we also excluded papers on advertising and branding (e.g., Hopp and Gangadharbatla, 2016 ; Yaoyuneyong et al., 2016 ) or active catalogues ( Rese et al., 2014 ). We further excluded research that does not model the consumer process ( Tan et al., 2021 ). We kept only empirical studies with a quantitative methodology, leaving 62 papers. We decided to exclude research with a qualitative approach (e.g., Olsson et al., 2013 ; Scholz and Duffy, 2018 ; Romano et al., 2021 ) from our analysis because these papers cannot be integrated into our systematic reviewing approach. Still, we will use these papers to enrich the evaluation and interpretation of the state-of-the-art in the discussion section. Finally, we excluded papers that did not pass certain predefined quality standards (e.g., no statistical inference tests or very small sample sizes). After this cleansing process, the set was reduced to 52 suitable papers.

In a third step, we inspected the references of the various AR articles and the latest issues of journals that frequently publish AR-related articles in marketing and consumer research. We include four additional papers, ending up with 56 papers for our systematic literature review.

The papers are published in journals with a focus on Marketing, Retailing, Information Science, and Technology. The highest share of papers in the literature review was published in the Journal of Retailing and Consumer Services (17), followed by the Journal of Business Research (8), Journal of the Academy of Marketing Science (3), Journal of Retailing (2), Journal of Interactive Marketing (2), Psychology and Marketing (2), Internet Research (2), International Journal of Advertising (2), International Journal of Retail and Distribution Management (2), and Journal of Fashion Marketing and Management (2). Single papers were identified in several other outlets: Asia Pacific Journal of Marketing and Logistics, Computers in Human Behavior, Cyberpsychology, Behavior, and Social Networking, Informatics, Information Technology and People, Electronic Commerce Research and Applications, International Journal of Human-Computer Interaction, International Journal of Information Management, International Journal of Semantic Computing, Journal of Electronic Commerce Research, Journal of Internet Commerce, Journal of Marketing Management, Technological Forecasting, and Social Change, and Transactions on Marketing Research. The number of papers increases in the last years (2008: 1, 2014: 1, 2015: 1, 2016: 1, 2017: 6, 2018: 6, 2019: 10, 2020: 12, 2021: 16, 2022: 2).

Data analysis

We divide the data analysis process into four thematic sections: applications, theories, consumer processing models, and methods. First, in terms of applications, we explore the contexts in which the AR technology is applied (e-/m-commerce vs. brick-and-mortar). We analyze for which product categories ASR is applied, which ASR functionality is relevant (informing, visualizing, trying-on, placing), and which devices are used (stationary monitors, PC/laptops, mobile device, or head-mounted and wearable devices). Second, we summarize the theoretical basis of the analyzed studies. Third, we identify the main components of the consumer research models that were addressed in the reviewed papers. These models can be formally decomposed into 1) predictors, 2) the mediator variables that capture the underlying process, 3) the outcomes, and 4) the moderator variables that capture relevant boundary conditions or contingencies. Beyond their formal position in the consumer-processing model, we systemize the variables with respect to their conceptual contribution. Specifically, we distinguish the two major categories that capture the defining properties of the AR technology, namely, augmentation and interaction. We also consider three categories that describe consumers’ mental processing (user experience, perceived user benefits, and concerns/barriers). The remaining categories refer to technology acceptance and consumption behavior. Finally, we examine the research methods (survey, experiments) and type of manipulation (if applicable).

Applications

In the first step of the review, we examine how AR is used in retailing settings from the perspective of applications and functionalities. As outlined in Table 2 , we consider various retail settings (e-/m-commerce vs. physical stores; Carboni and Hagbeg, 2019 ) as past research has not yet systematically compared these perspectives, which may enable and require different AR functionalities. We then investigate the product categories that have been researched so far. The categories are extracted through an inductive process while reviewing the literature. Next, we consider the AR functionalities building on the classification developed above (informing, visualizing, trying-on, and placing). Finally, we consider the AR devices, including monitors, PC/laptops, mobile devices, and head-mounted devices ( Flavián et al., 2019 ). While assigning the reviewed articles to these categories is a rather descriptive process, we explore—for the first time in a systematic manner—the contingencies of the retailing setting, the product categories, the AR functions, and the applied devices.

TABLE 2 . Applications of AR in retailing.

In Table 2 , we provide an overview of our coding of the current body of ASR research. About three-quarters of the reviewed studies address AR technologies in e-commerce or m-commerce (e.g., Javornik, 2016b ; Baek et al., 2018 ; Lee et al., 2021 ). Only seven studies focus on the AR technology’s application in brick-and-mortar retailing (e.g., Joerß et al., 2021 ; Yuan et al., 2021 ; Hoffmann et al., 2022 ). Six studies focus neither on e-/m-commerce nor on physical retailing. These studies, for instance, consider more generally the use of AR glasses (e.g., Rauschnabel, 2018 ; Rauschnabel et al., 2018 ) or tangible (vs. gesture-based) interactions with an AR system (e.g., Brito and Stoyanova, 2018 ).

In Table 3 , we detail the examined product categories and related AR functionalities, finding a systematic confound between the analyzed product category and the AR function. In particular, studies exploring the trying-on function often use existing virtual try-on-applications for apparel, accessories, or makeup. Likewise, several studies use existing furniture planners for virtually placing furniture in one’s own rooms at home.

TABLE 3 . Product category and AR functions.

To visualize these results and illustrate how the various applications of AR in retailing are embedded in a broader network, we employed network analysis ( Figure 1 ). Originally used to assess social networks ( Hennig et al., 2012 ), this analytical technique recently became popular to conduct literature reviews (e.g., Hoffmann et al., 2020 ). This analysis visualizes contingencies, which enables us to identify gaps and it builds the foundation for developing our future research agenda. We converted our coding in Table 2 into a 19 × 19 matrix and used this as the starting point for the analysis. The diagonal of this matrix captures the total number of studies that explored this AR factor, while the non-diagonal elements reflect the frequency with which a pair of two factors was investigated in prior research. In the analysis, each AR factor is represented by a node, the size of which indicates the relative frequency with which this factor was studied in the literature. Occurrences of the factor with other AR-related factors in previous studies are illustrated by ties, the size of which indicates the frequency of their co-occurrence. Spatially close relationships in the resulting graphical network consequently indicate which AR factors were studied together in the current literature. More distant relationships concern factors that received less attention or were explored in isolation.

FIGURE 1 . Network analysis of the AR-related factors studied in previous research. Notes. ● Type of retailing, ■ product category, ▲ AR function, ◆ device.

The complete graphical network is represented in Figure 1 , showing that ASR is predominantly investigated in the e-commerce domain in order to try on (or test) products, both for mobile devices or traditional PC and laptops. Several strong ties with the trying-on function accordingly show that this AR function was primarily examined in the fashion industry, cosmetics, and accessories. Furniture is explored in regards to placing. A visual inspection further reveals that conveying information is an understudied functionality of AR. Likewise, the technology appears less central in traditional brick-and-mortar stores, such as enriching grocery purchases. Consequently, devices provided by the company—which may become increasingly relevant in physical shopping contexts like head-mounted displays—received much less attention than devices owned by the consumer. The frequency and diversity of the ties for computers and smartphones imply that research relying on these devices is already rich and heterogeneous but also hints at certain gaps in the literature that will be discussed in our research agenda in greater detail.

In sum, the visualization in Figure 1 depicts the contingencies among the product category, the AR functionality, the retailing channel, and the AR device. These contingencies plague the current body of empirical AR literature and thus reflect the practical challenges when conducting AR research in the past. Still, it is important to mention that different combinations are feasible that may provide so far untapped benefits. When the technological development and diffusion of AR devices (such as head-mounted displays) proceeds, novel applications will emerge and be the subject of research interest (e.g., shopping goods offered in brick-and-mortar stores can be virtually placed in the consumers’ house via head-mounted displays).

The current AR research relies on a rich fundament of well-established theories. Most of our analyzed studies draw on a sound theoretical basis. These papers’ research objectives largely determined the applied theoretical basis. As a general framework, some of them use the S-O-R model ( Mehrabian and Russel, 1974 ). A large number of papers builds on the technology acceptance model ( Davis. 1989 ) or its extensions (e.g., UTUAT) to explain the adoption of the AR technology. Others apply the information systems success model ( DeLone and McLean, 1992 ). Research on the drivers of AR adoption and purchase intentions frequently adopts motivational theories, such as flow ( Csikszentmihalyi, 1997 ), the theory of interactive media effects ( Sundar et al., 2015 ), uses and gratification theory ( Ruggiero, 2000 ), regulatory mode theory ( Higgins et al., 2003 ), or self-determination theory ( Ryan and Deci, 2000 ). Some AR studies specifically focus on the fact that consumers can observe themselves wearing products with the help of the technology. These studies rely on self-referencing ( Rogers et al., 1977 ) and virtual liminoid ( Jung and Pawlowski, 2014 ). Further articles consider whether and how consumers are able to imagine products or their placement better when supported by an AR tool. These papers build on mental imagery ( Schifferstein, 2009 ) and situated cognition ( Robbins and Aydede, 2009 ). For example, the situated cognition perspective posits that consumers more deeply process and remember information when this information is embedded in their environment (e.g., virtually placing furniture in their own living room) and when consumers interact with the information (e.g., actively controlling the angle of the 3D visualization; Hilken et al., 2017 , 2018 ). Finally, some studies take into account the risks and barriers of AR adoption, building on equity theory ( Adams, 1963 ) or the privacy calculus theory ( Culnan and Armstrong, 1999 ).

Consumer models

Past ASR research has been guided by different objectives, such as predicting customer experience, understanding technology adoption, or improving downstream marketing outcomes. Accordingly, the consumer models used so far are cluttered. In this section, we restructure the body of empirical literature systematically to depict the overlaps of the involved variables across the AR studies conducted in the different contexts. On this basis, we will then integrate these partial models.

First, we give an overview of the independent, mediating, moderating, and dependent variables in the consumer behavior models in ASR. The scopes of the different studies vary substantially. For example, while some papers seek to explain purchase intentions as the primary outcome (e.g., Baek et al., 2018 ; Fan et al., 2020 ), others focus on the perceived ease of use as the outcome variable (e.g., Mishra et al., 2021 ). Notably, some studies conceptualize ease of use as a predictor (e.g., Zhang et al., 2019 ), whereas others specify this as a mediator (e.g., Plotkina and Saurel, 2019 ) that translates into purchase intentions as the dependent variable. We will now discuss the function of different variables from the perspective of the individual studies (Are they predictors, mediators, moderators, or outcomes?) before we start reorganizing the variables into an integrative framework.

The predictor variables in the consumer models refer to the factors augmentation, interaction, user experience, user benefit, and concerns/barriers. In terms of augmentation, many studies compare an experimental treatment involving AR to a control group without AR, such as a traditional website of the same brand. Several studies measure the user’s perception of augmented quality as the predictor. In the interaction category, the variables interactivity and stimulated control are frequently analyzed. For user experience as a measured predictor variable, perceived ease of use, aesthetics or visual quality and perceived enjoyment are most often applied. User benefits are analyzed in terms of perceived usefulness, information quality as well as utilitarian and hedonic benefit. Perceived privacy risks are often conceptualized as concerns or barriers.

To explain the process and induced mechanisms when interacting with the AR technology, the extant studies specified mediator variables , which comprise the categories user experience, user benefit, concerns/barriers, and consumption behavior. In the user experience category, many studies capture perceived ease of use, perceived enjoyment and the feeling of spatial presence or telepresence as mediating variables. As user benefit, prior research modelled the perceived usefulness as well as the utilitarian and hedonic benefits. By contrast, perceived intrusiveness is a relevant concern or barrier that explains some users’ reluctance to adopt AR. The literature also suggests mediators that are not specific to the AR technology. This concerns consumption behavior, including brand-related variables like self-brand-connection or brand engagement.

The outcome variables of the consumer models include various aspects of user experience, technology acceptance, and consumption behavior. For user experience, relevant outcomes involve shopping enjoyment or a positive experience. The most widely analyzed variables of technology acceptance are attitude towards the AR and the intention to use it. With regard to consumption behavior, most researchers apply a measurement of purchase intention. Other relevant outcome variables in this category concern brand attitude and word-of-mouth.

Some studies also include moderating variables and boundary conditions that help understand when AR is effective and when not. Moderating variables include aspects of the product, such as product type ( Poushneh, 2018 ; Rauschnabel et al., 2019 ; Fan et al., 2020 ; Mishra et al., 2021 ), product contextuality ( Heller et al., 2019a ), consumer’s brand attachment ( Yuan et al., 2021 ), and price-value trade-off ( Heller et al., 2019a ). Other moderators relate to the national background ( Pantano et al., 2017 ) or sociodemographics ( Zhang et al., 2019 ). Some studies rely on consumer-centred moderators, including technology anxiety ( Kim and Forsythe 2008 ), technology-as-solution-belief ( Joerß et al., 2021 ), involvement ( Kim and Forsythe 2008 ), AR familiarity/experience ( Yim et al., 2017 ; Song et al., 2019 ; Bonnin, 2020 ), processing fluency ( Hilken et al., 2017 ; Heller et al., 2019a ), or assessment orientation ( Heller et al., 2019b ; Jessen et al., 2020 ) as moderators.

We have reorganized the variables in Figure 2 according to their theoretical function in the models (e.g., explaining user experience, technology acceptance, marketing outcomes, etc.). We also indicate whether these variables were initially featured as predictors, mediators, outcomes, or moderators. This re-organization of the variables builds the basis for our theory development towards an integrative consumer-processing model of the AR technology in shopping contexts.

FIGURE 2 . Conceptually-structured overview of the researched variables. Notes. Superscripted numbers indicate the study number (see Table 2 ). Pred = variable used as a predictor variable in the cited study; med = variable used as a mediator in the cited study; out = variable used as an outcome variable in the cited study.

An overview of the methods applied in AR research is presented in Table 4 . The 56 reviewed papers report 85 quantitative studies in total. As shown in Table 4 , 31 papers report surveys and thus correlational data, while 30 papers report experiments. Note that two papers include both survey and experimental research. No clear dominance emerged for the mode of data collection, which happened both online (26 papers) and in the lab (25). Five papers even report evidence from field studies.

TABLE 4 . Methods.

Researchers took different approaches to manipulate AR usage ( Table 4 ). In the most straightforward approach (23 papers), the participants were asked to use an AR system before completing a survey (e.g., Huang and Liao, 2015 , Huang and Liao, 2017 ; McLean and Wilson, 2019 ; van Esch et al., 2019 ; Park and Yoo, 2020 ). Typically, the participants were directed to an existing AR application and asked to download it on their smartphones. This is important to mention because these studies did not include a systematic experimental manipulation with different treatments and/or a control group. Hence, the evidence is of a correlational nature, which needs to be taken into consideration when drawing conclusions.

The second cluster of papers compared the AR system to another system, such as a website with AR and the same website without this technology (e.g., Javornik, 2016b ; Yim et al., 2017 ; Beck and Crié, 2018 ; Bonnin, 2020 ; Watson et al., 2020 ). In their make-up study, Smink et al. (2019) compared the AR with pictures of the participants and pictures of a model. In a within-subject experiment, Baytar et al. (2020) compared physical try-on and then virtual try-on of apparel. Again, most of these studies made use of existing AR tools as the experimental treatment. It is important to distinguish this cluster of papers from the previous one because a systematic and standardized manipulation was, often, possible because several companies host a website where the AR condition can be switched on or off (virtual try-ons; e.g., Poushneh, 2018 ; Javornik, 2016b ; Smink et al., 2019 ). Interestingly, some of these studies find that AR is superior, while others report the opposite ( Plotkina and Saurel, 2019 ), which hints that AR effects are complex and contingent on several factors. For this reason, we will discuss the potential moderator variables later when we develop an integrative framework.

While the papers in the second cluster primarily focus on AR’s overall effectiveness compared to other communication modes, those of the third cluster zoomed in on the AR technology. These papers experimentally manipulated theoretically relevant aspects of the technology, such as the degree of interactivity ( Poushneh and Vasquez-Parraga, 2017 ) and controllability ( Hoffmann et al., 2022 ), markerless vs. marker-based interaction ( Brito & Stoyanova, 2018 ), or the sensory control mode ( Heller et al., 2019a ).

Finally, some researchers ran multi-factorial experiments that manipulated various factors of the AR or one AR factor and a context factor. For example, Hilken et al. (2017) manipulated the stimulated physical control (low/high) and the environmental embedding (low/high). Baek et al. (2018) crossed the AR perspective (self-vs. other-viewing) and two levels of narcissism (high vs. low). Heller et al. (2019b) crossed imagery transformation (low/high) and embedding (low/high) as well as product contextuality (no/yes). Hoffmann et al. (2022) manipulated the AR controllability (low/high) and the AR information detailedness (low/high).

Theory development

Need and rationale of the integrative framework.

Footing on the findings of our review, we now contribute to theory development for AR in retailing settings. Our review pinpoints that previous AR research relies on a solid fundament of well-established theories in the information systems domain, innovation management, and marketing (e.g., technology acceptance model). The field also borrows from related disciplines, such as communication science, social psychology, and cognitive psychology (e.g., uses and gratification theory, flow theory, situated cognition). This breadth and depth of the theoretical grounding attest to the different lenses through which the AR technology is already explored. However, our literature review revealed that the applied theoretical foundations are fragmented and often not AR-specific. This emphasizes the need to synthesize the fragmented theoretical basis and develop an AR-specific theoretical basis. Relatedly, ASR research should extend beyond studying the adaption-based factors and the Technology Acceptance Model (TAM), which is very useful to assess the usability and adoption of the technology but is also not specific to the two core constitutional properties of the AR technology, namely, augmentation and interactivity.

As another consequence of the specific foci, the literature review has revealed that scholars from the various fields adapt different theories with different constructs to explain AR effects. Psychological research is interested in the flow experience during AR usage, while innovation and technology management scholars often study technology acceptance as a relevant evaluation criterion and thus dependent variable. Notably, other disciplines are interested in the more downstream outcomes. Marketing and retailing scholars, for example, would conceptualize these variables as mediators that serve to explain the marketing-relevant variables, such as purchase behavior, loyalty, or word-of-mouth.

Against this background, we suggest an integrative framework. Inspired by similar attempts to integrate partial theories in other fields, such as wearable technologies (e.g., Kalantari 2017 ; Chuah, 2019 ), our theory development rests on a synopsis and refinement of extant work. We integrate conceptual works ( Heller et al., 2019b ; Caboni and Hagberg, 2019 ), qualitative research ( Olsson et al., 2013 ; Scholz and Duffy, 2018 ; Romano et al., 2021 ), literature overviews ( Bonnetti et al., 2018 ; Lavoye et al., 2021 ), and the relevant theoretical foundations. We substantiate this with our summary of empirical findings ( Figure 2 ). We identify the most relevant aspects and integrate them into a new theoretical framework that serves as a guideline for AR research and practice in shopping contexts.

Based on our literature review, we detect several unresolved issues relevant to our theory development. The fragmentation of extant approaches stresses the need to integrate the partial theories and account for contextual aspects. While previous papers have either considered AR in e-commerce (e.g., Javornik, 2016b ) or brick-and-mortar retailing (e.g., Joerß et al., 2021 ), our integrative model integrates findings from both perspectives and includes several moderator variables to account for boundary conditions. Second, as a major theoretical contribution, we distinguish between different features of AR in retailing settings, including informing, visualizing, trying-on, and placing. We detail the contingencies between these AR functionalities and other relevant variables, such as the shopping context, devices, product types, and customer benefit.

Given this holistic inclusion of different variable types, scholars can flexibly apply the framework to different settings. It is noteworthy that not every variable is relevant in every setting. Thus, the framework can be simplified and adapted to the specific context.

Suggestion of an integrative framework

Figure 3 presents the proposed integrative framework. The process-oriented model starts with the technology design. The next steps involve the consumer’s mental processing and adoption of the technology. This paper focuses on ASR, so the outcome variables involve shopping-related consumer reactions and moderators to understand the boundary conditions of the AR technology and its effects.

FIGURE 3 . Framework model of consumer behavior in augmented shopping reality.

For the technology design , we follow previous conceptualizations and distinguish the two key properties of AR: augmentation and interaction (e.g., Azuma, 1997 ; Azuma et al., 2001 ; Javornik, 2016a ). Augmentation concerns the question of which features are augmented and how they are embedded. Hence, we refer to literature that has focussed on the embedding of elements in AR (e.g., Hilken et al., 2017 ). Our model extends these approaches by including all the relevant aspects that can be augmented in shopping settings, including the information, the product, the self, or the environment. This distinction maps onto the ASR functions informing, visualizing, trying-on, and placing. Interaction refers to consumers’ ability to control the virtual elements, such as choosing additional information, transforming visual 2D or 3D elements, etc. This also includes the modalities of the interaction, such as touching, voice-based, or gesture-based.

In terms of consumer’s mental processing , three aspects should be distinguished: user experience, user benefits, and concerns/barriers. While positive user experience and perceived user benefits positively influence downstream variables, the concerns and barriers will hinder technology adoption, negatively impacting marketing outcomes. Core aspects of the user experience involve perceived enjoyment, spatial/telepresence, flow experience, and perceived ease of use. According to the technology acceptance model, user’s experience will also improve perceptions of user benefits (e.g., Huang and Liao, 2015 ; Pantano et al., 2017 ; Rese et al., 2017 ; Zhang et al., 2019 ). The user benefits of AR have initially been described as rather hedonic. However, with a more widespread use of AR as a serious tool that helps consumers make consumption decisions in today’s shopping environments, perceived usefulness and utilitarian aspects will become more relevant. This concerns information delivery, decision support or being a recommendation agent that simplifies consumers’ decisions. Further aspects include sensual and social benefits. Finally, as concerns and barriers to adopting the technology, the perceptions of privacy risks are relevant. Various sensors are active during the AR use (cameras, microphones, GPS information, tracking of the human/device interaction), so consumers’ privacy concerns are a critically relevant topic. Furthermore, there might be a perceived loss of autonomy or a sense of being manipulated, apart from feared side effects (e.g., the impact on the user’s physical or mental health or the implications for other consumers).

As downstream variables , the model includes the most relevant constructs from a marketing perspective, namely 1) whether consumers accept and adopt the technology and 2) whether using the technology alters marketing outcomes (e.g., purchasing). The technology acceptance includes, in particular, consumers’ attitudes towards ASR as well as the (re)use intention. The marketing outcomes include brand attitudes, purchase intentions, word-of-mouth, and loyalty.

Notably, the sequence of the model is not necessarily unidirectional. The user experience evaluation requires consumers first to try the technology (or at least observe others trying it), so user experience can also be conceptualized as a downstream variable in some cases. To avoid disproportionately inflating the framework’s complexity, we focus on the substantive perspective of retailing managers and a long-term perspective. From this perspective, the most relevant direction of the impact operates from the user experience via rational benefits/cost calculation to the technology attitude and ultimately the intention to (re)use the technology. This sequence matches the conceptual models of the previous studies that have already combined user experiences and/or user benefits with (re)use intentions (e.g., Pantano et al., 2017 ; Rese et al., 2017 ; Yim et al., 2017 ; Plotkina and Saurel, 2019 ; Smink et al., 2019 ; Zhang et al., 2019 ; Bonnin, 2020 ; Park and Yoo, 2020 ; Watson et al., 2020 ; Qin et al., 2021b ; Lee et al., 2021 ).

The model also integrates six relevant categories of moderators and boundary conditions . These moderators have not yet been systematically tested, but they appear to be relevant based on our integration of past empirical works enriched with previous conceptual considerations ( Flavián et al., 2019 ) and qualitative studies ( Olsson et al., 2013 ; Scholz and Duffy, 2018 ; Romano et al., 2021 ). First, the retailing channel is an important boundary condition of consumers’ processing of the ASR ( Hilken et al., 2018 ; Caboni and Hagberg, 2019 ). Our review revealed that the ASR functionality is contingent on the retailing channel. In e- and m-commerce, visualizing and virtual try-ons are important functions. In brick-and-mortar retailing, the ability to provide more product information seems more relevant. Second, how consumers process the ASR will further depend on aspects of the technology, such as the AR device that overlays the physical world with virtual information. Consumers may react differently depending on whether the information is displayed on a stationary monitor (e.g., “magic mirror”), handheld-mobile device, wearable and head-mounted devices, or even implanted lenses ( Flavián et al., 2019 ). Third, the user benefit and usefulness of the ASR functionality (informing, visualizing, trying-on, placing) are contingent on the product type. Products with search, credence, and experience qualities require different treatments ( Girard and Dion, 2010 ). Fourth, the shopping situation is important too ( Olsson et al., 2013 ). Relevant aspects are the shopping goal, product involvement, and the social surrounding (private or public shopping in e-commerce or brick-and-mortar retailing). Fifth, complexity is an interesting moderator as past research has shown that, in digital contexts, consumers prefer medium degrees of complexity ( Geissler et al., 2001 ; Mai et al., 2014 ) because fewer complexity evokes boredom, while higher levels evoke the feeling of being overwhelmed. Complexity may hence evoke a curvilinear moderating effect on several variables included in our processing model, such as the perceived ease of use, perceived enjoyment, loss of autonomy, presence, and flow. Scholars and ASR designers need to find the optimal degree of complexity for the AR design, product, and shopping task. Finally, the AR effects depend on consumer traits, such as technology attitudes, innovativeness, AR experience, and processing fluency.

Research agenda

ASR researchers can take the suggested framework ( Figure 3 ) as orientation when developing new study designs. Based on our review and the synthesis of the analyzed literature, we propose directions for future research. Moreover, our literature review points to notable methodological limitations and gaps in the research landscape that need to be addressed.

Ten recommendations for future ASR research

First, most empirical studies in our literature review were conducted in e-commerce and m-commerce settings where websites were enriched by AR (e.g., Beck and Crié, 2018 ; Yim and Park, 2019 ). Only few papers consider ASR in brick-and-mortar stores (e.g., van Esch et al., 2019 ; Joerß et al., 2021 ; Hoffmann et al., 2022 ). This limited focus on digital environments may stem from the wide use of AR in these environments and, thus, the ease of studying them. Still, a rapidly growing number of AR apps exist for physical environments and deserve much greater attention. Our review has shown that the application and effectiveness of AR in shopping environments are highly contextualized and depend on the specific AR functions, devices, product types, and so forth. So far, researchers have adopted theories fitting the particular context in which they conduct their AR studies. Most retailing studies focus on the hedonic benefits of the try-on function, while the benefits of placing and visualizing are less explored. Arguably, the AR function ‘informing’ is more relevant for utilitarian benefits ( Hoffmann et al., 2021 ). However, AR-specific theories for information processing have not yet been applied, so more research is needed to fill this void. Ideally, studies should explicitly model the retailing channel as a moderating variable. In e-commerce, for example, the virtual try-on function is greatly valued for certain products (e.g., apparel, accessories, or cosmetics) and can create hedonic benefits for shoppers. Still, theories that explain consumer reactions in these contexts (e.g., via flow experience) cannot necessarily explain consumer reactions to AR-delivered information functions through mobile applications for groceries in brick-and-mortar settings (e.g., Joerß et al., 2021 ). Here, utilitarian benefits (e.g., transparent and trustworthy information) may be more important in the consumer’s decision-making compared to hedonic benefits and flow experiences.

Second, and related to the previous direction for future research, the AR practice and research in shopping contexts has not yet made full use of AR’s vast range of functionalities. We outlined that the four functionalities informing, visualizing, trying-on, and placing are most relevant for shopping contexts. So far, we see that informing is mainly used for food products in brick-and-mortar contexts, while trying-on and placing are more frequently used in studies on e-commerce. Trying-on is applied for product categories, such as cosmetics, apparel, or glasses, whereas placing is used for furniture or wall paint. Evidently, the AR functions are more flexible, so scholars and practitioners should consider different configurations of AR functionalities in combination with certain product categories and retailing channels.

Third, more research on the role of the AR-enabling device is needed. Mobile devices, like tablets and smartphones, are already widespread and common in use. While the current theoretical approaches apply to these devices, other devices like head-mounted displays, AR glasses, or even AR lenses are still unusual in real-life, everyday applications. Nonetheless, the diffusion of such devices may intensify, and managers need to know how consumers respond to such devices and whether they use them to facilitate their judgments and shopping decisions in online and offline contexts. In addition, nuanced explanations are needed given the differences across devices, including the control function (e.g., haptic or voice), the need to use hands (smartphones vs. glasses), or the ability to move around (PC vs. helmet). For instance, location-based AR-effects (shopping guide in the supermarket, mall) should be embedded in future ASR-specific theories.

Fourth and related to the previous aspect, augmented information can be conveyed through different modalities that address different senses, including visual formats (e.g., images, labels), audio formats (e.g., music, voice), audio-visual formats (videos), and 2D or 3D visualizations (e.g., Azuma, 1997 ; Javornik, 2016a ; Brito and Stoyanova, 2018 ). Marketing research has mainly considered visual augmentations but largely neglected other modalities. Exceptions are Brito and Stoyanova (2018) testing markerless or gesture-based interactions, Huang and Liao (2017) considering haptic imagery, and Heller et al. (2019b) comparing touch control vs. voice control ( Heller et al., 2019b ). More research spanning a more comprehensive array of presentation modes and interaction forms is necessary to prepare the application of advanced technologies in the future.

Fifth, ASR research should also take into account systematic differences among product types, for example, by distinguishing search, experience, and credence goods. For some goods, hedonic experiences through AR usage might entertain consumers (e.g., entertainment products, cosmetics), but information and more utilitarian aspects should be relevant in other consumer decisions or purchases. This distinction is relevant because experiential and materialistic purchases are motivated in distinct ways ( Gilovich et al., 2015 ), so the various AR-functionalities should be differentially relevant too. Further theory development should also include value theories that distinguish, for example, functional, hedonic/experiential or social values that are enjoyed while using AR ( Hilken et al., 2020 ). Deeper theoretical considerations of the consumers’ decision modes (extensive, limited, habitual, impulsive) may further help understand how beneficial consumers experience the AR support to be. Future advances may also need to be able to explain ASR effects at different stages of the customer journey (e.g., Jessen et al., 2020 ). Findings of stage-specific effects in other domains ( Mai et al., 2021 ) imply that the AR-functionalities may be differently relevant across the various consumption stages. For example, while the hedonic experience may motivate users to use the AR technology in the first place, the more utilitarian benefits that materialize with each purchase may become the driving force to encourage continued use of the technology in shopping environments. Future ASR research may therefore require multi-stage theories.

Sixth, more contextual and situational factors should be taken into account too. Consumers’ mood, time pressure, or the presence of other consumers may determine whether (or not) consumers are willing to make use of AR. Plausibly, consumers rely on the AR technology when they have time for their shopping but abandon it when being under pressure or in more stressful shopping environments ( Hoffmann et al., 2022 ). Such knowledge would be important because the technology should unfold its benefit of being a tool or a recommendation agent, especially under such suboptimal conditions. Additionally, when used with mobile devices (smartphones or tablets), the AR technology can also integrate location-based information ( Reitmayr and Drummond, 2006 ). This is already implemented in tourism apps and may likewise be beneficial in other marketing applications. Furthermore, contextual information might be available from other modalities, such as haptic and olfactory information. Future studies should consider such cross-modal effects.

Seventh, our synopsis of past research in the framework model shows that research explaining the (re)use intention of ASR has quite matured, and there are also indications of positive influences on some downstream variables once consumers start using ASR. However, a lack of research persists on how ASR usage translates into marketing outcomes, such as brand image, (re)purchase intention, or word of mouth. We call for more research on the mediating variables and the specific boundary conditions. For example, the ethnographic study by Scholz and Duffy (2018) reveals that ASR can create a more intimate brand-customer relationship. Future research may build on this finding to delve deeper into how ASR can shape brand image, customer-brand relationship, and brand equity. The qualitative research of Romano et al. (2021) has demonstrated that ASR affects users’ consideration and their choice set. Accordingly, more insights into the decision process are needed.

Eighth, since ASR is a relatively young domain, the longitudinal perspective is still missing but very promising. Future research could start, for example, by investigating the adaptation and learning processes of AR users. Although several purported benefits of the technology are supported by empirical evidence, the question remains to be answered as to whether consumers integrate the technology into their daily shopping routines (and if so, how they do this). For example, do consumers consider ASR only as a toy that creates entertaining and hedonic benefits at the moment but which can wear out quickly? Or will ASR—with greater diffusion and familiarity—become a tool that consumers use for information and utilitarian needs on a regular basis? Scholars should therefore study habituation and even potential wear-out effects. Another interesting development, which may become more widespread once the technology has further evolved, is the extension or even substitution of physical products by virtual products as discussed by Rauschnabel (2021) and Dwivedi et al. (2021) .

Ninth, it is principally possible to contextualize, customize, and personalize the AR information for increasing convenience and consumer benefit (e.g., Huynh et al., 2019 ; Hsu et al., 2021 ; Nikhashemi et al., 2021 ). However, data privacy and security are crucial topics that deserve attention ( Hilken et al., 2017 ; Inman and Nikolova, 2017 ; Rauschnabel et al., 2018 ; Smink et al., 2019 ). Future AR research should explore the perceived intrusiveness, loss of autonomy, the willingness to share personal data (i.e., regarding facial recognition or location-based information for personalization), and whether consumers are willing to use ASR apps on their personal smartphones or other devices. Some studies have already relied on equity theory ( Adams 1963 ) to understand how customers balance augmentation quality and the privacy of personal information ( Poushneh 2018 ), but more insights are needed to manage this issue better.

Tenth, research on ASR will be an ongoing process as the technology evolves rapidly. Similarly, organizational and legal conditions will change. Marketing and retailing literature should monitor, for example, which devices are the prime candidates for ASR in future business environments. Also, more experimental technological solutions should be explored, such as lenses or implants, and how consumers will respond to these developments. Likewise, insights are needed into how AI, big data, and machine learning alter the information provided via ASR. Especially concerning consumer trust, knowledge is sparse about who should provide the ASR information (producer, retailer, NGOs, other consumers).

Limitations and methodological aspects

First, a vast majority of the current ASR research explores the effectiveness of the technology (contrasting AR to other technologies) and zooms in on specific properties. However, no study has tackled the factors leading to actual purchase behavior. Therefore, future research should shift the focus more strongly on real purchases (consumer perspective) and actual sales (company perspective, e.g., Tan et al., 2021 ), both in e-commerce and brick-and-mortar stores. Obtaining purchase data in the field will help corroborate the ecological validity of previous findings and provide a more precise estimate of the AR technology’s impact in real shopping environments.

Second, the research designs in the AR literature have certain limitations regarding the sampling. Many studies employ nonsystematic sampling procedures, such as convenience sampling or snowballing, for recruiting participants. Several studies also take advantage of university participation pools. This may be explained by the fact that current designs emphasize internal validity, but external validity should be taken into focus too. As a result of previous sampling procedures, our state of the knowledge is often based on studies with students and younger consumers who are often technology-affine and open to digital solutions. However, older individuals are also very relevant in digital and physical environments. In a similar vein, education levels are typically higher for student samples. A gender bias might also arise and distort our conclusions about ASR. Due to the focus on virtual try-on for apparel and cosmetics, nine of the 56 studies (16%) include female participants only and some other studies include substantially more female than male participants. Considering these specifics and the lack of representativeness, AR research should be complemented by studies in the field that put greater emphasis on external and ecological validity.

Third, this literature review shows that the number of quantitative empirical studies analyzing consumer behavior in ASR is steadily growing. Meta-analyses could provide a valuable approach to integrate findings and estimate general effects and moderations. Yet, quantitative meta-analyses require a small set of pre-defined predictors and outcome variables. Our review reveals that prior studies included about 40 different predictor variables and about 30 different outcome variables (plus about 40 mediating variables). This variety reflects that ASR researchers tried to explain different outcomes (e.g., user experience, technology acceptance, shopping and patronage behavior), and they used very diverse theories to explain these outcomes (e.g., TAM, flow, Uses and Gratifications etc.). Our approach of structuring the literature and integrating different models into a more comprehensive model hopefully sets the stage for future meta-analyses.

Reviewing and synthesizing a comprehensive set of empirical papers on ASR reveals a growing interest in this technology in both research and practice. Prior research efforts have been devoted to consumers’ experience with AR, acceptance of AR, and behavioral reactions to AR in various online and offline retailing settings. However, our literature review also revealed that large differences exist in the studied AR devices, the AR functionalities, and the addressed consumer reactions. It is therefore not surprising that the literature is highly fragmented. The integrated framework developed in this paper can help researchers to approach ASR and its effects holistically as well as to explore the relevant moderators that cause different effects in different situations. However, more research is needed on these moderators, in particular, and the effects of context (e.g., with regard to the devices, addressed senses, retailing channels, shopping goals, product categories, etc.). From a methodological point of view, more laboratory and field experiments are needed to learn more about the causal effects of different ASR designs. The present literature review and the outlined research directions will hopefully guide scholars and provide knowledge to optimize future ASR, being beneficial for both retailers and customers.

Summary statement of contribution

The growing literature on consumer behavior in augmented shopping reality (ASR) is highly fragmented as scholars focus on different retailing settings, products categories, AR functions, and AR devices, using different methods. Our systematic literature review synthesizes the knowledge and critically discusses the empirical studies, both conceptually and methodologically. In addition, the paper develops an integrative framework model, which helps derive directives for future research on consumer behavior in ASR.

Data availability statement

The original contributions presented in the study are cited in the text and reference list, further inquires can be directed to the corresponding author.

Author contributions

SH and RM contributed to conception and design of the study. SH conducted the literature research and review and wrote the first draft of the manuscript. SH and RM contributed to manuscript revision, read, and approved the submitted version.

Acknowledgments

We acknowledge financial support by the Deutsche Forschungsgemeinschaft (DFG) within the funding programme Open Access-Publikationskosten.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Adams, J. S. (1963). Towards an understanding of inequity. J. Abnorm. Soc. Psychol. 67 (5), 422–436. doi:10.1037/h0040968

PubMed Abstract | CrossRef Full Text | Google Scholar

Aluri, A. (2017). Mobile augmented reality (MAR) game as a travel guide: Insights from pokémon GO. J. Hosp. Tour. Technol. 8 (1), 55–72. doi:10.1108/jhtt-12-2016-0087

CrossRef Full Text | Google Scholar

Arghashi, V., and Yuksel, C. A. (2022). Interactivity, inspiration, and perceived usefulness! How retailers’ AR-apps improve consumer engagement through flow. J. Retail. Consumer Serv. 64, 102756. doi:10.1016/j.jretconser.2021.102756

Azuma, R. T. (1997). A survey of augmented reality. Presence. (Camb). 6 (4), 355–385. doi:10.1162/pres.1997.6.4.355

Azuma, R. T., Baillot, Y., Behringer, R., Feiner, S., Julier, S., and MacIntyre, B. (2001). Recent advances in augmented reality . Washington, DC: Naval Research Lab .

Google Scholar

Baek, T. H., Yoo, C. Y., and Yoon, S. (2018). Augment yourself through virtual mirror: The impact of self-viewing and narcissism on consumer responses. Int. J. Advert. 37 (3), 421–439. doi:10.1080/02650487.2016.1244887

Barhorst, J. B., McLean, G., Shah, E., and Mack, R. (2021). Blending the real world and the virtual world: Exploring the role of flow in augmented reality experiences. J. Bus. Res. 122, 423–436. doi:10.1016/j.jbusres.2020.08.041

Baytar, F., Chung, T., and Shin, E. (2020). Evaluating garments in augmented reality when shopping online. J. Fash. Mark. Manag. 24 (4), 667–683. doi:10.1108/jfmm-05-2018-0077

Beck, M., and Crié, D. (2018). I virtually try it … I want it ! Virtual fitting room: A tool to increase on-line and off-line exploratory behavior, patronage and purchase intentions I want it! Virtual fitting room: A tool to increase online and offline exploratory behavior, patronage and purchase intentions. J. Retail. Consumer Serv. 40, 279–286. doi:10.1016/j.jretconser.2016.08.006

Berryman, D. R. (2012). Augmented reality: A review. Med. Ref. Serv. Q. 31 (2), 212–218. doi:10.1080/02763869.2012.670604

Bonetti, F., Warnaby, G., and Quinn, L. (2018). “Augmented reality and virtual reality in physical and online retailing: A review, synthesis and research agenda,” in Augmented reality and virtual reality . Editors T. M. Jung, and C. tom Dieck ( Springer ), 119–132.

Bonnin, G. (2020). The roles of perceived risk, attractiveness of the online store and familiarity with AR in the influence of AR on patronage intention. J. Retail. Consumer Serv. 52, 101938. doi:10.1016/j.jretconser.2019.101938

Botella, C. M., Lizandra, M. C. J., Baños, R. M., Raya, M. A., Guillén, V., and Rey, B. (2005). Mixing realities? An application of augmented reality for the treatment of cockroach phobia. Cyberpsychology Behav. 8 (2), 162–171. doi:10.1089/cpb.2005.8.162

Bower, M., Howe, C., McCredie, N., Robinson, A., and Grover, D. (2014). Augmented reality in education–cases, places and potentials. Educ. Media Int. 51 (1), 1–15. doi:10.1080/09523987.2014.889400

Boyd, D. E., and Koles, B. (2019). Virtual reality and its impact on B2B marketing: A value-in-use perspective. J. Bus. Res. 100, 590–598. doi:10.1016/j.jbusres.2018.06.007

Brito, P. Q., and Stoyanova, J. (2018). Marker versus markerless augmented reality. Which has more impact on users? Int. J. Human–Computer. Interact. 34, 819–833. doi:10.1080/10447318.2017.1393974

Caboni, F., and Hagberg, J. (2019). Augmented reality in retailing: A review of features, applications and value. Int. J. Retail Distribution Manag. 47 (11), 1125–1140. doi:10.1108/ijrdm-12-2018-0263

Carmigniani, J., Furht, B., Anisetti, M., Ceravolo, P., Damiani, E., and Ivkovic, M. (2011). Augmented reality technologies, systems and applications. Multimed. Tools Appl. 51 (1), 341–377. doi:10.1007/s11042-010-0660-6

Castillo, S. M. J., and Bigne, E. (2021). A model of adoption of AR-based self-service technologies: A two country comparison. Int. J. Retail Distribution Manag. 49 (7), 875–898. doi:10.1108/ijrdm-09-2020-0380

Chae, H. J., Hwang, J. I., and Seo, J. (2018). “Wall-based space manipulation technique for efficient placement of distant objects in augmented reality,” in Proceedings of the 31st Annual ACM Symposium on User Interface Software and Technology , 45–52.

Chen, P., Liu, X., Cheng, W., and Huang, R. (2017). A review of using Augmented Reality in education from 2011 to 2016. Innovations Smart Learn. , 13–18.

Choi, U., and Choi, B. (2020). The effect of augmented reality on consumer learning for search and experience products in mobile commerce. Cyberpsychology, Behav. Soc. Netw. 23 (11), 800–805. doi:10.1089/cyber.2020.0057

Chuah, S. H. W. (2019). Wearable XR-technology: Literature review, conceptual framework and future research directions. Int. J. Technol. Mark. 13 (3-4), 1–259. doi:10.1504/ijtmkt.2019.10021794

Chung, N., Lee, H., Kim, J. Y., and Koo, C. (2018). The role of augmented reality for experience-influenced environments: The case of cultural heritage tourism in Korea. J. Travel Res. 57 (5), 627–643. doi:10.1177/0047287517708255

Csikszentmihalyi, M. (1997). Finding flow: The psychology of engagement with everyday life . New York, NY: Basic Books .

Culnan, M. J., and Armstrong, P. K. (1999). Information privacy concerns, procedural fairness, and impersonal trust: An empirical investigation. Organ. Sci. 10 (1), 104–115. doi:10.1287/orsc.10.1.104

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13 (3), 319–340. doi:10.2307/249008

de Ruyter, K., Heller, J., Hilken, T., Chylinski, M., Keeling, D. I., and Mahr, D. (2020). Seeing with the customer’s eye: Exploring the challenges and opportunities of AR advertising. J. Advert. 49 (2), 109–124. doi:10.1080/00913367.2020.1740123

Deliza, R., and MacFie, H. J. (1996). The generation of sensory expectation by external cues and its effect on sensory perception and hedonic ratings: A review. J. Sens. Stud. 11 (2), 103–128. doi:10.1111/j.1745-459x.1996.tb00036.x

DeLone, W. H., and McLean, E. R. (1992). Information systems success: The quest for the dependent variable. Inf. Syst. Res. 3 (1), 60–95. doi:10.1287/isre.3.1.60

Di Serio, A., Ibáñez, M. B., and Kloos, C. D. (2013). Impact of an augmented reality system on students’ motivation for a visual art course. Comput. Educ. 68, 586–596. doi:10.1016/j.compedu.2012.03.002

Dwivedi, Y. K., Ismagilova, E., Hughes, D. L., Carlson, J., Filieri, R., Jacobson, J., et al. (2021). Setting the future of digital and social media marketing research: Perspectives and research propositions. Int. J. Inf. Manag. 59, 102168. doi:10.1016/j.ijinfomgt.2020.102168

Fan, X., Chai, Z., Deng, N., and Dong, X. (2020). Adoption of augmented reality in online retail-ing and consumers’ product attitude: A cognitive perspective. J. Retail. Consumer Serv. 53 (2), 101986. doi:10.1016/j.jretconser.2019.101986

Flavián, C., Ibáñez-Sánchez, S., and Orús, C. (2019). The impact of virtual, augmented and mixed reality technologies on the customer experience. J. Bus. Res. 100, 547–560. doi:10.1016/j.jbusres.2018.10.050

Geissler, G., Zinkhan, G., and Watson, R. T. (2001). Web home page complexity and communi-cation effectiveness. J. Assoc. Inf. Syst. 2 (2), 1–48. doi:10.17705/1jais.00014

Gilovich, T., Kumar, A., and Jampol, L. (2015). A wonderful life: Experiential consumption and the pursuit of happiness. J. Consum. Psychol. 25 (1), 152–165. doi:10.1016/j.jcps.2014.08.004

Girard, T., and Dion, P. (2010). Validating the search, experience, and credence product classification framework. J. Bus. Res. 63 (9-10), 1079–1087. doi:10.1016/j.jbusres.2008.12.011

Hacker, J., vom Brocke, J., Handali, J., Otto, M., and Schneider, J. (2020). Virtually in this together–how web-conferencing systems enabled a new virtual togetherness during the COVID-19 crisis. Eur. J. Inf. Syst. 29 (5), 563–584. doi:10.1080/0960085x.2020.1814680

Hamari, J., Malik, A., Koski, J., and Johri, A. (2019). Uses and gratifications of pokémon go: Why do people play mobile location-based augmented reality games? Int. J. Human–Computer. Interact. 35 (9), 804–819. doi:10.1080/10447318.2018.1497115

Harley, J. M., Poitras, E. G., Jarrell, A., Duffy, M. C., and Lajoie, S. P. (2016). Comparing virtual and location-based augmented reality mobile learning: Emotions and learning outcomes. Educ. Technol. Res. Dev. 64 (3), 359–388. doi:10.1007/s11423-015-9420-7

Heller, J., Chylinski, M., de Ruyter, K., Mahr, D., and Keeling, D. I. (2019a). Let me imagine that for you: Transforming the retail frontline through augmenting customer mental imagery ability. J. Retail. 95 (2), 94–114. doi:10.1016/j.jretai.2019.03.005

Heller, J., Chylinski, M., de Ruyter, K., Mahr, D., and Keeling, D. I. (2019b). Touching the untouchable: Exploring multi-sensory augmented reality in the context of online retailing. J. Retail. 95 (4), 219–234. doi:10.1016/j.jretai.2019.10.008

Hennig, M., Brandes, U., Borgatti, S. P., Pfeffer, J., and Mergel, I. (2012). Studying social networks: A guide to empirical research . Frankfurt, Germany: Campus Verlag .

Herz, M., and Rauschnabel, P. A. (2019). Understanding the diffusion of virtual reality glasses: The role of media, fashion and technology. Technol. Forecast. Soc. Change 138, 228–242. doi:10.1016/j.techfore.2018.09.008

Higgins, E. T., Kruglanski, A. W., and Pierro, A. (2003). “Regulatory mode: Locomotion and assessment as distinct orientations,” in Advances in experimental social psychology . Editor M. P. Zanna (Cambridge, MA: Academic Press ), 293–344.

Hilken, T., de Ruyter, K., Chylinski, M., Mahr, D., and Keeling, D. I. (2017). Augmenting the eye of the beholder: Exploring the strategic potential of augmented reality to enhance online service experiences. J. Acad. Mark. Sci. 45 (6), 884–905. doi:10.1007/s11747-017-0541-x

Hilken, T., Heller, J., Chylinski, M., Keeling, D. I., Mahr, D., and de Ruyter, K. (2018). Making omnichannel an augmented reality: The current and future state of the art. J. Res. Interact. Mark. 12 (4), 509–523. doi:10.1108/jrim-01-2018-0023

Hilken, T., Keeling, D. I., de Ruyter, K., Mahr, D., and Chylinski, M. (2020). Seeing eye to eye: Social augmented reality and shared decision making in the marketplace. J. Acad. Mark. Sci. 48 (2), 143–164. doi:10.1007/s11747-019-00688-0

Hinsch, C., Felix, R., and Rauschnabel, P. A. (2020). Nostalgia beats the wow-effect: Inspiration, awe and meaningful associations in augmented reality marketing. J. Retail. Consumer Serv. 53, 101987. doi:10.1016/j.jretconser.2019.101987

Hoffmann, N. C., Symmank, C., Mai, R., Stok, F. M., Rohm, H., and Hoffmann, S. (2020). The influence of extrinsic product attributes on consumers’ food decisions: Review and network analysis of the marketing literature. J. Mark. Manag. 36 (9-10), 888–915. doi:10.1080/0267257x.2020.1773514

Hoffmann, S., Joerß, T., Mai, R., and Akbar, P. (2022). Augmented reality-delivered product information at the point of sale: When information controllability backfires. J. Acad. Mark. Sci. 50, 743–776. doi:10.1007/s11747-022-00855-w

Hoffmann, S., Mai, R., and Pagel, T. (2021). Toy or tool? Utilitaristischer und hedonischer nutzen mobiler augmented-reality-apps. HMD Praxis der Wirtschaftsinformatik 59 (1), 23–36. doi:10.1365/s40702-021-00822-z

Hopp, T., and Gangadharbatla, H. (2016). Novelty effects in augmented reality advertising environments: The influence of exposure time and self-efficacy. J. Curr. Issues Res. Advert. 37 (2), 113–130. doi:10.1080/10641734.2016.1171179

Hsu, S. H. Y., Tsou, H. T., and Chen, J. S. (2021). Yes, we do. Why not use augmented reality?” customer responses to experiential presentations of AR-based applications. J. Retail. Consumer Serv. 62, 102649. doi:10.1016/j.jretconser.2021.102649

Huang, T.-L., and Liao, S. (2015). A model of acceptance of augmented-reality interactive technology: The moderating role of cognitive innovativeness. Electron. Commer. Res. 15 (2), 269–295. doi:10.1007/s10660-014-9163-2

Huang, T. L., and Liao, S. L. (2017). Creating e-shopping multisensory flow experience through augmented-reality interactive technology. Internet Res. 27 (2), 449–475. doi:10.1108/intr-11-2015-0321

Hudson, S., Matson-Barkat, S., Pallamin, N., and Jegou, G. (2019). With or without you? Interaction and immersion in a virtual reality experience. J. Bus. Res. 100, 459–468. doi:10.1016/j.jbusres.2018.10.062

Huynh, B., Ibrahim, A., Chang, Y. S., Höllerer, T., and O’Donovan, J. (2019). User perception of situated product recommendations in augmented reality. Int. J. Semant. Comput. 13 (3), 289–310. doi:10.1142/s1793351x19400129

Inman, J. J., and Nikolova, H. (2017). Shopper-facing retail technology: A retailer adoption decision framework incorporating shopper attitudes and privacy concerns. J. Retail. 93 (1), 7–28. doi:10.1016/j.jretai.2016.12.006

Javornik, A. (2016a). Augmented reality: Research agenda for studying the impact of its media characteristics on consumer behaviour. J. Retail. Consumer Serv. 30, 252–261. doi:10.1016/j.jretconser.2016.02.004

Javornik, A. (2016b). ‘It’s an illusion, but it looks real!’ Consumer affective, cognitive and behavioural responses to augmented reality applications. J. Mark. Manag. 32 (9-10), 987–1011. doi:10.1080/0267257x.2016.1174726

Jessen, A., Hilken, T., Chylinski, M., Mahr, D., Heller, J., Keeling, D. I., et al. (2020). The playground effect: How augmented reality drives creative customer engagement. J. Bus. Res. 116, 85–98. doi:10.1016/j.jbusres.2020.05.002

Jiang, Y., Wang, X., and Yuen, K. F. (2021). Augmented reality shopping application usage: The influence of attitude, value, and characteristics of innovation. J. Retail. Consumer Serv. 63, 102720. doi:10.1016/j.jretconser.2021.102720

Joerß, T., Hoffmann, S., Mai, R., and Akbar, P. (2021). Digitalization as solution to environmental problems? When users rely on augmented reality-recommendation agents. J. Bus. Res. 128, 510–523. doi:10.1016/j.jbusres.2021.02.019

Jung, Y., and Pawlowski, S. D. (2014). Understanding consumption in social virtual worlds: A sensemaking perspective on the consumption of virtual goods. J. Bus. Res. 67 (10), 2231–2238. doi:10.1016/j.jbusres.2014.01.002

Kaasinen, E., Schmalfuß, F., Özturk, C., Aromaa, S., Boubekeur, M., Heilala, J., et al. (2020). Empowering and engaging industrial workers with Operator 4.0 solutions. Comput. Industrial Eng. 139, 105678. doi:10.1016/j.cie.2019.01.052

Kalantari, M. (2017). Consumers' adoption of wearable technologies: Literature review, synthesis, and future research agenda. Int. J. Technol. Mark. 12 (3), 1–307. doi:10.1504/ijtmkt.2017.10008634

Kim, H. C., and Hyun, M. Y. (2016). Predicting the use of smartphone-based augmented reality (AR): Does telepresence really help? Comput. Hum. Behav. 59, 28–38. doi:10.1016/j.chb.2016.01.001

Kim, J., and Forsythe, S. (2008). Adoption of virtual try-on technology for online apparel shopping. J. Interact. Mark. 22 (2), 45–59. doi:10.1002/dir.20113

Kourouthanassis, P., Boletsis, C., Bardaki, C., and Chasanidou, D. (2015). Tourists responses to mobile augmented reality travel guides: The role of emotions on adoption behavior. Pervasive Mob. Comput. 18, 71–87. doi:10.1016/j.pmcj.2014.08.009

Kowalczuk, P., Siepmann, C., and Adler, J. (2021). Cognitive, affective, and behavioral consumer responses to augmented reality in e-commerce: A comparative study. J. Bus. Res. 124, 357–373. doi:10.1016/j.jbusres.2020.10.050

Kytö, M., Ens, B., Piumsomboon, T., Lee, G. A., and Billinghurst, M. (2018). “Pinpointing: Precise head- and eye-based target selection for augmented reality,” in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems , 1–14.

Lavoye, V., Mero, J., and Tarkiainen, A. (2021). Consumer behavior with augmented reality in retail: A review and research agenda. The International Review of Retail, Distribution and Consumer Research 31 (3), 299–329. doi:10.1080/09593969.2021.1901765

Lee, H., Xu, Y., and Porterfield, A. (2021). Consumers' adoption of AR-based virtual fitting rooms: From the perspective of theory of interactive media effects. J. Fash. Mark. Manag. 25 (1), 45–62. doi:10.1108/jfmm-05-2019-0092

Mai, R., Hoffmann, S., and Balderjahn, I. (2021)., When drivers become inhibitors of organic consumption: The need for a multistage view J. Acad. Mark. Sci. 49. forthcoming, 1151–1174. doi:10.1007/s11747-021-00787-x

Mai, R., Hoffmann, S., Schwarz, U., Niemand, T., and Seidel, J. (2014). The shifting range of optimal web site complexity. J. Interact. Mark. 28 (2), 101–116. doi:10.1016/j.intmar.2013.10.001

Marr, B. (2020). The top 10 technology trends in retail: How tech will transform shopping in 2020. Forbes . Available at: https://www.forbes.com/sites/bernardmarr/2019/11/25/the-top-10-technology-trends-in-retailhow-tech-will-transform-shopping-in-2020/#5e4e4a0a4e03 .

Masood, T., and Egger, J. (2019). Augmented reality in support of Industry 4.0. Implementation challenges and success factors. Robotics Computer-Integrated Manuf. 58, 181–195. doi:10.1016/j.rcim.2019.02.003

Mauroner, O., Le, L., and Best, S. (2016). Augmented reality in advertising and brand communication: An experimental study. Int. J. Inf. Commun. Eng. 10 (2), 422–425.

McLean, G., and Wilson, A. (2019). Shopping in the digital world: Examining customer engage-ment through augmented reality mobile applications. Comput. Hum. Behav. 101 (8), 210–224. doi:10.1016/j.chb.2019.07.002

Mehrabian, A., and Russell, J. A. (1974). An approach to environmental psychology . MIT Press .

Mishra, A., Shukla, A., Rana, N. P., and Dwivedi, Y. K. (2021). From “touch” to a “multisensory” experience: The impact of technology interface and product type on consumer responses. Psychol. Mark. 38 (3), 385–396. doi:10.1002/mar.21436

Moher, D., Liberati, A., Tetzlaff, J., and Altman, D. G.Prisma Group. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 6 (7), e1000097. doi:10.1371/journal.pmed.1000097

Nagele, A. N., Bauer, V., Healey, P. G., Reiss, J. D., Cooke, H., Cowlishaw, T., et al. (2021). Interactive audio augmented reality in participatory performance. Front. Virtual Real. 1, 610320. doi:10.3389/frvir.2020.610320

Narumi, T., Nishizaka, S., Kajinami, T., Tanikawa, T., and Hirose, M. (2011). “MetaCookie+,” in In 2011 IEEE Virtual Reality Conference ( IEEE ), 265–266.

Narvar, (2017). Making returns a competitive advantage . Available at: https://see.narvar.com/report-making-returns-a-competitive-advantage-2017-q2.html .

Nikhashemi, S. R., Knight, H. H., Nusair, K., and Liat, C. B. (2021). Augmented reality in smart retailing: A (n)(A) symmetric approach to continuous intention to use retail brands’ mobile AR apps. J. Retail. Consumer Serv. 60, 102464. doi:10.1016/j.jretconser.2021.102464

Olsson, T., Lagerstam, E., Kärkkäinen, T., and Väänänen-Vainio-Mattila, K. (2013). Expected user experience of mobile augmented reality services: A user study in the context of shopping centres. Pers. Ubiquitous Comput. 17 (2), 287–304. doi:10.1007/s00779-011-0494-x

Palmatier, R. W., Houston, M. B., and Hulland, J. (2018). Review articles: Purpose, process, and structure. J. Acad. Mark. Sci. 46, 1–5. doi:10.1007/s11747-017-0563-4

Pantano, E., Rese, A., and Baier, D. (2017). Enhancing the online decision-making process by using augmented reality: A two country comparison of youth markets. J. Retail. Consumer Serv. 38 (5), 81–95. doi:10.1016/j.jretconser.2017.05.011

Park, M., and Yoo, J. (2020). Effects of perceived interactivity of augmented reality on consumer responses: A mental imagery perspective. J. Retail. Consumer Serv. 52, 101912. doi:10.1016/j.jretconser.2019.101912

Plotkina, D., and Saurel, H. (2019). Me or just like me? The role of virtual try-on and physical appearance in apparel M-retailing. J. Retail. Consumer Serv. 51, 362–377. doi:10.1016/j.jretconser.2019.07.002

Poushneh, A. (2018). Augmented reality in retail: A trade-off between user’s control of access to personal information and augmentation quality. J. Retail. Consumer Serv. 41 (2), 169–176. doi:10.1016/j.jretconser.2017.12.010

Poushneh, A., and Vasquez-Parraga, A. Z. (2017). Discernible impact of augmented reality on retail customer’s experience, satisfaction and willingness to buy. J. Retail. Consumer Serv. 34 (2), 229–234. doi:10.1016/j.jretconser.2016.10.005

Qasem, Z. (2021). The effect of positive TRI traits on centennials adoption of try-on technology in the context of E-fashion retailing. Int. J. Inf. Manag. 56, 102254. doi:10.1016/j.ijinfomgt.2020.102254

Qin, H., Osatuyi, B., and Xu, L. (2021a). How mobile augmented reality applications affect continuous use and purchase intentions: A cognition-affect-conation perspective. J. Retail. Consumer Serv. 63, 102680. doi:10.1016/j.jretconser.2021.102680

Qin, H., Peak, D. A., and Prybutok, V. (2021b). A virtual market in your pocket: How does mobile augmented reality (MAR) influence consumer decision making? J. Retail. Consumer Serv. 58 (1), 102337–102656. doi:10.1016/j.jretconser.2020.102337

Rauschnabel, P. A. (2021). Augmented reality is eating the real-world! the substitution of physical products by holograms. Int. J. Inf. Manag. 57, 102279. doi:10.1016/j.ijinfomgt.2020.102279

Rauschnabel, P. A., Felix, R., and Hinsch, C. (2019). Augmented reality marketing: How mobile AR-apps can improve brands through inspiration. J. Retail. Consumer Serv. 49 (7), 43–53. doi:10.1016/j.jretconser.2019.03.004

Rauschnabel, P. A., He, J., and Ro, Y. K. (2018). Antecedents to the adoption of augmented reality smart glasses: A closer look at privacy risks. J. Bus. Res. 92 (11), 374–384. doi:10.1016/j.jbusres.2018.08.008

Rauschnabel, P. A., Rossmann, A., and tom Dieck, M. C. (2017). An adoption framework for mobile augmented reality games: The case of Pokémon Go. Comput. Hum. Behav. 76, 276–286. doi:10.1016/j.chb.2017.07.030

Rauschnabel, P. A. (2018). Virtually enhancing the real world with holograms: An exploration of expected gratifications of using augmented reality smart glasses. Psychol. Mark. 35 (8), 557–572. doi:10.1002/mar.21106

Reitmayr, G., and Drummond, T. W. (2006). “Going out: Robust model-based tracking for outdoor augmented reality,” in 2006 IEEE/ACM international symposium on mixed and augmented reality , 109–118.

Rese, A., Baier, D., Geyer-Schulz, A., and Schreiber, S. (2017). How augmented reality apps are accepted by consumers: A comparative analysis using scales and opinions. Technol. Forecast. Soc. Change 124 (6), 306–319. doi:10.1016/j.techfore.2016.10.010

Rese, A., Schreiber, S., and Baier, D. (2014). Technology acceptance modeling of augmented reality at the point of sale: Can surveys be replaced by an analysis of online reviews? J. Retail. Consumer Serv. 21 (5), 869–876. doi:10.1016/j.jretconser.2014.02.011

Robbins, P., and Aydede, M. (2009). “A short primer on situated cognition,” in The cambridge handbook of situated cognition . Editors P. Robbins, and M. Aydede (New York: Cambridge University Press ), 3–10.

Rogers, T. B., Kuiper, N. A., and Kirker, W. S. (1977). Self-reference and the encoding of personal information. J. Personality Soc. Psychol. 35 (9), 677–688. doi:10.1037/0022-3514.35.9.677

Romano, B., Sands, S., and Pallant, J. I. (2021). Augmented reality and the customer journey: An exploratory study. Australas. Mark. J. 29 (4), 354–363. doi:10.1016/j.ausmj.2020.06.010

Ruggiero, T. E. (2000). Uses and gratifications theory in the 21st century. Mass Commun. Soc. 3 (1), 3–37. doi:10.1207/s15327825mcs0301_02

Ryan, R. M., and Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. Am. Psychol. 55 (1), 68–78. doi:10.1037/0003-066x.55.1.68

Saleem, M., Kamarudin, S., Shoaib, H. M., and Nasar, A. (2021). Retail consumers’ behavioral intention to use augmented reality mobile apps in Pakistan. J. Internet Commer. 21, 497–525. doi:10.1080/15332861.2021.1975427

Samsung Business Insights (2020). 5 retail technology trends to watch in 2020 . Available at: https://insights.samsung.com/2020/01/03/5-retail-technology-trends-to-watch-in-2020/ .

Schifferstein, H. N. J. (2009). Comparing mental imagery across the sensory modalities. Imagination, Cognition Personality 28 (4), 371–388. doi:10.2190/ic.28.4.g

Scholz, J., and Duffy, K. (2018). We ARe at home: How augmented reality reshapes mobile marketing and consumer-brand relationships. J. Retail. Consumer Serv. 44, 11–23. doi:10.1016/j.jretconser.2018.05.004

Skarbez, R., Smith, M., and Whitton, M. C. (2021). Revisiting milgram and kishino's reality-virtuality continuum. Front. Virtual Real. 2, 27. doi:10.3389/frvir.2021.647997

Smink, A. R., Frowijn, S., van Reijmersdal, E. A., van Noort, G., and Neijens, P. C. (2019). Try online before you buy: How does shopping with augmented reality affect brand responses and personal data disclosure. Electron. Commer. Res. Appl. 35, 100854. doi:10.1016/j.elerap.2019.100854

Smink, A. R., van Reijmersdal, E. A., van Noort, G., and Neijens, P. C. (2020). Shopping in augmented reality: The effects of spatial presence, personalization and intrusiveness on app and brand responses. J. Bus. Res. 118, 474–485. doi:10.1016/j.jbusres.2020.07.018

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. J. Bus. Res. 104, 333–339. doi:10.1016/j.jbusres.2019.07.039

Song, H. K., Baek, E., and Choo, H. J. (2019). Try-on experience with augmented reality comforts your decision: Focusing on the roles of immersion and psychological ownership. Inf. Technol. People 33 (4), 1214–1234. doi:10.1108/itp-02-2019-0092

Spreer, P., and Kallweit, K. (2014). Augmented reality in retail: Assessing the acceptance and potential for multimedia product presentation at the PoS. Trans. Mark. Res. 1 (1), 23–31. doi:10.15764/mr.2014.01002

Sundar, S. S., Jia, H., Waddell, T. F., and Huang, Y. (2015). “Toward a theory of interactive media effects (TIME),” in The handbook of the psychology of communication technology . Editor S. Sundar (Chichester: John Wiley & Sons ), 47–86.

Tan, Y. C., Chandukala, S. R., and Reddy, S. K. (2021). Augmented reality in retail and its impact on sales. J. Mark. 86, 48–66. doi:10.1177/0022242921995449

Teas, R. K., and Agarwal, S. (2000). The effects of extrinsic product cues on consumers’ perceptions of quality, sacrifice, and value. J. Acad. Mark. Sci. 28 (2), 278–290. doi:10.1177/0092070300282008

tom Dieck, M. C., and Jung, T. (2018). A theoretical model of mobile augmented reality acceptance in urban heritage tourism. Curr. Issues Tour. 21 (2), 154–174. doi:10.1080/13683500.2015.1070801

van Berlo, Z. M., van Reijmersdal, E. A., Smit, E. G., and van der Laan, L. N. (2021). Brands in virtual reality games: Affective processes within computer-mediated consumer experiences. J. Bus. Res. 122, 458–465. doi:10.1016/j.jbusres.2020.09.006

van Esch, P., Arli, D., Gheshlaghi, M. H., Andonopoulos, V., von der Heidt, T., and Northey, G. (2019). Anthropomorphism and augmented reality in the retail environment. J. Retail. Consumer Serv. 49 (7), 35–42. doi:10.1016/j.jretconser.2019.03.002

van Krevelen, D. W. F., and Poelman, R. (2010). A survey of augmented reality technologies, applications and limitations. Int. J. Virtual Real. 9 (2), 1–20. doi:10.20870/ijvr.2010.9.2.2767

Vávra, P., Roman, J., Zonča, P., Ihnát, P., Němec, M., Kumar, J., et al. (2017). Recent development of augmented reality in surgery: A review. J. Healthc. Eng. , 1–9. doi:10.1155/2017/4574172

vXchange, (2020). Top 7 augmented reality statistics for 2020 . Available at: https://www.vxchnge.com/blog/augmented-reality-statistics .

Vynz Research (2020). Augmented reality and virtual reality market . Available at: https://www.vynzresearch.com/ict-media/augmented-reality-and-virtual-realitymarket .

Wang, Y., Ko, E., and Wang, H. (2021). Augmented reality (AR) app use in the beauty product industry and consumer purchase intention. Asia Pac. J. Mark. Logist. 34, 110–131. doi:10.1108/APJML-11-2019-0684

Ward, R. J., Jjunju, F. P. M., Griffith, E. J., Wuerger, S. M., and Marshall, A. (2020). Artificial odour-vision syneasthesia via olfactory sensory argumentation. IEEE Sens. J. 21 (5), 6784–6792. doi:10.1109/jsen.2020.3040114

Ward, R. J., Wuerger, S., and Marshall, A. (2021). Smelling sensations: Olfactory crossmodal correspondences. J. Percept. Imaging 4, 0–12. doi:10.2352/j.percept.imaging.2021.4.2.020402

Watson, A., Alexander, B., and Salavati, L. (2020). The impact of experiential augmented reality applications on fashion purchase intention. Int. J. Retail Distribution Manag. 48 (5), 433–451. doi:10.1108/ijrdm-06-2017-0117

WBR (2020). How new tech is creating seamless mobile shopping experiences! . Available at: https://etailwest.wbresearch.com/downloads/how-new-tech-is-creating-seamless-mobile-shopping-experiences-etail (Accessedon July 14th, 2020).

Wedel, M., Bigné, E., and Zhang, J. (2020). Virtual and augmented reality: Advancing research in consumer marketing. Int. J. Res. Mark. 37 (3), 443–465. doi:10.1016/j.ijresmar.2020.04.004

Westbrook, G., and Angus, A. (2021). Top 10 global consumer trends 2021 . Euromonitor International . Available at: https://go.euromonitor.com/white-paper-EC-2021-Top-10-Global-Consumer-Trends.html .

Whang, J. B., Song, J. H., Choi, B., and Lee, J. H. (2021). The effect of Augmented Reality on purchase intention of beauty products: The roles of consumers’ control. J. Bus. Res. 133, 275–284. doi:10.1016/j.jbusres.2021.04.057

Yaoyuneyong, G., Foster, J., Johnson, E., and Johnson, D. (2016). Augmented reality marketing: Consumer preferences and attitudes toward hypermedia print ads. J. Interact. Advert. 16 (1), 16–30. doi:10.1080/15252019.2015.1125316

Yim, M. Y. C., Chu, S. C., and Sauer, P. L. (2017). Is augmented reality technology an effective tool for e-commerce? An interactivity and vividness perspective. J. Interact. Mark. 39 (3), 89–103. doi:10.1016/j.intmar.2017.04.001

Yim, M. Y. C., and Park, S.-Y. (2019). I am not satisfied with my body, So I like augmented reality (AR)”: Consumer responses to AR-based product presentations. J. Bus. Res. 100 (7), 581–589. doi:10.1016/j.jbusres.2018.10.041

Yoo, J. (2020). The effects of perceived quality of augmented reality in mobile commerce—an application of the information systems success model. Informatics 7 (2), 14–5824. doi:10.3390/informatics7020014

Yuan, C., Wang, S., Yu, X., Kim, K. H., and Moon, H. (2021). The influence of flow experience in the augmented reality context on psychological ownership. Int. J. Advert. 40, 922–944. doi:10.1080/02650487.2020.1869387

Zhang, T., Wang, W. Y. C., Cao, L., and Wang, Y. (2019). The role of virtual try-on technology in online purchase decision from consumers’ aspect. Internet Res. 29 (3), 529–551. doi:10.1108/intr-12-2017-0540

Zhou, F., Duh, H. B. L., and Billinghurst, M. (2008). “Trends in augmented reality tracking, interaction and display: A review of ten years of ismar,” in Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality ( IEEE Computer Society ), 193–202.

FIGURE A1 . Literature search process and papers per year.

Keywords: augmented reality, retailing, e-commerce, m-commerce, consumer behavior

Citation: Hoffmann S and Mai R (2022) Consumer behavior in augmented shopping reality. A review, synthesis, and research agenda. Front. Virtual Real. 3:961236. doi: 10.3389/frvir.2022.961236

Received: 04 June 2022; Accepted: 20 September 2022; Published: 14 October 2022.

Reviewed by:

Copyright © 2022 Hoffmann and Mai. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Stefan Hoffmann, [email protected]

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Please note you do not have access to teaching notes, global research trends in augmented reality: scientometric mapping based on scopus database.

Information Discovery and Delivery

ISSN : 2398-6247

Article publication date: 30 December 2021

Issue publication date: 13 October 2022

This study aims to present a scientometric analysis of publications related to “Augmented Reality.” In today’s Information Technology-driven era, augmented reality (A.R.) has evolved as a new immersive data source for developing knowledge combining authentic and digital images. Consequently, extensive research is going on “Augmented Reality” to discover its potential in knowledge development.

Methodology

The paper analyses and emphasizes the bibliographic data of Scopus articles with a suitable search query. The study was done concerning the chronological growth of research publications, highly cited publications, productive countries, prominent journals, prolific authors and institutions, author and country co-authorship network analysis and keywords analysis. The analysis was conducted by using open-source tools such as VOSViewer, Biblioshiny and Gephi.

The study reveals that a maximum number of publications on research in “Augmented Reality” are in the form of conference proceedings and articles. A.R., Virtual reality and A.R. application are the keywords with maximum number of occurrences. A significant number of publications are done in the USA, followed by Germany in the year 2020.

Originality/value

This study provides a precise idea of work done in different countries, a network of co-authorship between authors and countries, publication and citation impact of authors, journals, institutions and countries, year-wise progression and trending “augmented reality” topics research. This investigation will be advantageous for researchers and stakeholders to obtain rigorous bibliographic knowledge on literature related to the topic and work accordingly for R&D activities.

• Augmented reality
• A.R. application
• Augmented reality techniques
• Scientometric analysis
• Bibliometric analysis
• Information science

Borgohain, D.J. , Bhanage, D.A. , Verma, M.K. and Pawar, A.V. (2022), "Global research trends in augmented reality: scientometric mapping based on Scopus database", Information Discovery and Delivery , Vol. 50 No. 4, pp. 387-403. https://doi.org/10.1108/IDD-08-2021-0081

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The Trend of Metaverse and Augmented & Virtual Reality Extending to the Healthcare System

Kunal bhugaonkar.

1 Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Medical Sciences, Nagpur, IND

Roshan Bhugaonkar

2 Anesthesiology, Treat Me Hospital, Nagpur, IND

There is no escaping Internet's favorite buzzword for 2022: The Metaverse. Everyone is talking about it, but only a few know what it is or how it works. One can look at the Metaverse as a 3D model of the Internet where it is possible to spend your reality parallel to the virtual world. In broad terms, Metaverse can be explained as a virtual space, graphically rich, leaning towards verisimilitude where people can do all sorts of things they do in real-life such as shop, play, socialize, and party. The pandemic has accelerated innovations in the digital age. Looking beyond revolutions in telehealth, payments, remote monitoring, and secure data-sharing are other essential innovations in the fields of artificial intelligence (AI), virtual reality (VR), augmented reality (AR), and blockchain technology. The Metaverse is still in its nascent stage and evolving continuously, having a huge potential in health care to combine the technologies of AI, AR/VR, web 3.0, Internet of medical devices, and quantum computing, along with robotics to give a new direction to healthcare systems. From improving surgical precision to therapeutic usage and more, the Metaverse can bring significant changes to the industry

Introduction and background

Recent advances in VR technology have made it a very exciting and emerging field today [ 1 ]. In June 2020, neurosurgeons at Johns Hopkins University performed their very first AR surgery on a living patient. In the first procedure, physicians fixed six screws to fuse three vertebrae in the spine of a patient with severe debilitating back pain. In the second surgery, a cancerous tumor was removed from the patient's spine. During these surgeries, the surgeons wore headsets consisting of see-through eye displays which projected the images of the patient's interior anatomy based on already done computed tomography (CT) scans [ 2 , 3 ].

We are moving quickly towards the age of the Metaverse. The World Economic Forum already anticipates that one of the most revolutionary factors in transforming health care would be digital services [ 4 ]. With the COVID19 pandemic, telehealth went mainstream. Face-to-face contact was considered dangerous, and remote care became increasingly accepted [ 5 ]. Telepresence, digital twinning, and blockchain confluence are the three significant technical phenomena, according to futurist Bernard Marr, that have the potential to influence healthcare. These three ideas might be used to provide whole new methods of providing treatment, potentially reducing costs and significantly enhancing patient outcomes [ 6 ]. The greatest tech companies gradually began to aggressively engage in this previously unexplored region and assessed the numerous potential uses for the technology industry. Even Facebook formally changed its name to Meta, demonstrating its aspirational goal to become the dominant social media platform into a sizable Metaverse [ 7 ].

Metaverse includes the integration and overlapping of the digital and physical world, the integration of digital and real economies, the integration of digital and social life, the integration of digital and real identities, and the integration of digital with physical assets. It includes high-speed communication networks, the Internet of things (IoT), AR, VR, cloud computing, edge computing, blockchain, AI, and other technology. Technology is the driving factor that promotes the transition from the current Internet to Metaverse. The eight fundamental technologies are extended reality, user interaction (human-computer interaction), AI, blockchain, computer vision, IoT and robotics, edge and cloud computing, and future mobile network. There is still a big gap in achieving Metaverse transformation in the medical and health field. Existing platforms are still far from an ideal Health Metaverse, requiring the efforts of all parties [ 8 ]. Some of the well-known companies in the AR and VR market are Google, Microsoft, DAQRI, Psious, Mindmaze, Firsthand Technology, Medical Realities, Atheer, Augmedix, and Oculus VR [ 9 ].

It is expected that all these new concepts will greatly enhance comprehensive health care along with the prevention and treatment of diseases and will completely enhance the current model and usher in a new era in this industry. This review article sets out the viewpoint that VR/AR could be a new emphasis of direction in the development of training tools for medical education and communication skills for clinicians and medical students [ 10 ].

Digital world, digital patient

Medicine has always been a hands-on face-to-face personal experience. However, with the advent of newer technology, this is changing rapidly. It is true that AR and VR technologies have driven the gaming and entertainment industry, but it also has a very good potential to transform the healthcare industry since they can change a lot of traditional healthcare operations and branches in a variety of ways, including radiology, oncology, training, and more [ 11 ].

A field where AR/VR has proved to be particularly beneficial is therapy. Psychologists and psychiatrists use it to personalize environments for individual patients in aversion therapy, where patients interact with situations causing them anxiety in a controlled and safe environment where the interaction is monitored closely [ 12 ]. Surgical simulations, diagnostic imaging modality, patient care management, rehabilitative services, and healthcare management will be the earliest applications of the Metaverse. Patients can learn better about their disorders and treatment alternatives with this technology. In a clinical context, AR/VR can help nursing teams at the point of care. When AR is used with radiology, clinicians can display medical images, such as CT scan images, directly onto the patient and in arrangement with the patient's body, even when the person is moving, allowing them to examine interior anatomy more clearly [ 13 ].

Intravenous injections, for example, can benefit from technology like Accuvein's, which projects the map and plots the patient's own veins on their skin. Medtronic announced the acquisition of Digital Surgery, while Zimmer Biomet unveiled OptiVuTM Mixed Reality, employing HoloLens by Microsoft to blend the digital and real worlds. Through data interconnection, avatars will simulate realistic consultations, individualized treatment, diagnosis, and care [ 14 ]. Dimensional avatars of healthcare professionals will be able to interact with equipment like digital whiteboards in the Metaverse, and they will be able to make face-to-face contact without the use of complicated conference equipment. Before being used in a physical context, digital twins will be used to experiment and evaluate machines, systems, and procedures for flaws and vulnerabilities [ 15 ].

A digital twin can be described as a virtual model or simulation of any process, system, or object produced with real-world data in order to learn more about its real-world counterpart. The patient's digital twin may be the patient himself in the Metaverse. Individual digital twins will someday be utilized as test dummies to predict anything from surgery recovery to drug reactions. As our ability to map and understand individual DNA increases, this will become a reality [ 16 ]. Thanks to telemedicine consultations that particularly employ VR, patients will no longer be limited to being seen by a particular or specified doctor due to their present location. You can virtually be in the same room with the best specialist for your disease by simply putting on headsets even if you are physically on separate continents. Scans and testing can be done at your nearest physically accessible centers, and the results can be emailed to a specialist anywhere on the planet. It is especially useful in remote locations where medical personnel are scarce, and for patients in desolate areas who would otherwise have to travel vast distances to see a doctor [ 16 ].

It is obvious that the Metaverse is quite capable of linking real-time locations and objects utilized in the delivery of medical services and other virtual and physical things. The primary goal of the medical IoT is to complement traditional medical service delivery rather than replace it. It also aims to provide other routes that can digitally carry out physical tasks as effectively as possible when traditional methods are unavailable or inconvenient. First off, Metaverse will enhance both the effectiveness and the patient and physician experience of telemedicine (remote delivery of medical treatment). The Metaverse will make it possible for patients and physicians to interact virtually in real-time while in a virtual clinical setting using sensory teleportation items [ 17 ] (Figure ​ (Figure1). 1 ). Some physical examination techniques such as distant body observation, touch, auscultation, and vital sign collection are permissible in this environment. Through the use of the Metaverse, sophisticated surgical procedures may be carried out digitally with high levels of vision and precision by human surgeons and surgeon robots [ 18 ]. 

Image illustrated by the author

Medical education

Medical education and training will be altered by the usage of AR and VR. Students can virtually enter into the human body, giving them a thorough perspective while allowing them to replicate real-life treatments. Augmented Reality is also being used to provide students with hands-on learning opportunities, such as mimicking patient and surgical contacts so that medical interns may envision and practice newer techniques. Even more realistic experiences based on real surgery might be built, allowing students to experience surgery as if they were the surgeon themselves [ 19 ]. Learning will be changed into an immersive experience in which success will be rewarded, and data analytics will be used to target precision learning.

Since surgeries on cadavers are expensive for hospitals and have an impact on the students' tuition costs, the traditional medical school has limited resources for the practice of surgeries. But using VR in medical education allows students to train in a simulated setting for intensive surgical instruction at a substantially reduced cost [ 20 ] rather than just knowledge transmission (Figure ​ (Figure2). Advanced 2 ). Advanced hand skills and interactions, for example, necessitate additional technology in Metaverse-based healthcare training, which is more successful. Surgical intervention, for example, necessitates a thorough mastery of human anatomy and dexterity in grasping equipment. This involves the employment of appropriate tracking devices or software [ 21 ]. 

As doctors always seek more effective ways to complete surgeries with a greater success rate, technology could also be used in the Metaverse for difficult surgical operations. Doctors may estimate a patient's recovery period, any potential difficulties, and the necessary therapies for these complications using the data set gathered by the patient's digital twin as a preventative strategy [ 20 ].

Instructors are responsible for providing high-quality data that can be used in virtual programs to imitate on-site nursing competencies. In a clinical field experience program, learners should feel that there was no difference in clinical therapy after accomplishing this in the Metaverse context. It is a significant addition to health care that is expected to improve patient care in the long run [ 21 ]. The study is a real-world example of gamified learning. For each class attended, each video watched, and each assignment completed, users are rewarded with tokens, and some will be rewarded with non-fungible token (NFT) collectibles. Artificial Intelligence educators can use AR to show students how to stand, speak, and appear more confident. Students will learn greatly in a truly game-like situation with popular trainers demonstrating specific abilities using these methodologies. This may be a celebrity surgeon, with the surgeon receiving compensation for his instruction and the student receiving compensation for his learning [ 22 ].

However, putting this strategy into action will be a problem in and of itself. Current Metaverse rivals like Fortnight and Roblox, as well as Meta's Horizon Worlds and Microsoft's Mesh, are not compatible. One cannot transfer content gained, purchased, or made in Roblox to Horizon Worlds and vice versa, which goes against the Metaverse's original premise.

Medicine has traditionally been thought of as a human-to-human relationship. A patient's problem is initially discussed with a doctor. The doctor then determines the symptoms based on the patient's physiological data, such as emotional and physical responses, clinical data, and so on. Finally, the doctor determines which therapy option is best for the patient. Modern society has been revolutionized by the rise of big data and AI technology [ 8 ] which is not easy for everyone to digest. 

Since VR and AR information is primarily visual, the foundations are not established in the provision of medical services, which need the use of all five senses, including touch. Developers that conflate VR with the Metaverse add to the misunderstanding. There is no doubt that the Metaverse worldview will eventually replace many service domains. Change and convenience are also highly anticipated. Our medical professionals, on the other hand, must prioritize human dignity, respect for life, and affection for and treatment of the human body. To avoid human harm from the system, it will be required to thoroughly analyze all situations, such as policies, and prepare accordingly [ 23 ].

In the health Metaverse, there are still difficulties alongside opportunities. In the Metaverse, patient privacy and life safety raise numerous challenges and concerns. From a variety of angles, the health Metaverse will significantly alter medical practice. The Metaverse necessarily raises different concerns affecting the personal, public, and national security as a result of its enormous number of users and inventive connections. Inappropriate management of people's physical and mental health activities may jeopardize their health [ 8 ]. Not to mention, building the Metaverse in the early stage needs to consider protecting user privacy and physical and psychological safety. The Metaverse with massively connected devices and people, inevitably has significant loopholes in security, raising the question of what supervisory measures can ensure proper moral restraint. The technology stack of health Metaverse also shows the risks and difficulties of maintaining a system that cannot be compromised by hackers. Such risks threaten the personalized nature of the doctor-patient relationship [ 8 ].

The Metaverse also raises intriguing mental health concerns, such as the following [ 24 ]: VR dependency; transforming mental health therapy in general; and people suffering from psychosis, schizophrenia, sadness, or anxiety being harmed. Metaverse is currently mainly promoted by certain technology giants such as Facebook, Microsoft, and so on. At the time when Metaverse is ready, people inevitably accept all kinds of censorship and become victims of all sorts of commercial interests. Zhou et al. found that the design of the Metaverse business model is more biased towards platform owners, thereby weakening other competitors, which is often not conducive to the sustainable development of the platform [ 8 ].

It is safe to say that our reality is now very much augmented. But the medical industry took this premise to a whole new level that surpassed even our most optimistic expectations [ 25 ]. Global Metaverse in the healthcare market is anticipated to exhibit a stunning compound annual growth rate (CAGR) of 33.7% to reach a market size of US$ 7453.6 Million by the end of 2028 during the forecast period of 2022 to 2028. The global market size for Metaverse in healthcare is worth US$ 5056.4 Million in 2021 [ 26 ]. The various advantages of this new technology include a three-dimensional (3D) visualization, better understanding, a safe and controlled environment, more accurate explanation, uses in medical education, better and automated operative procedures, surgical operations and lots more. With time we will be seeing more uses coming up. Disadvantages include but are not limited to very expensive equipment, risk of addiction, concerns over privacy and security, and effects on mental health [ 27 - 33 ] (Table ​ (Table1 1 ).

[ 27 - 33 ]

Instead of merely reading the lengthy details on the bottle, patients may use AR to observe how a medicine works in 3D right in front of their eyes. With the use of AR technology, lab personnel could see their tests. Workers might begin working in pharmaceutical factories without any hands-on training since the machine would instruct them on what to do and how to accomplish it [ 34 ].

As healthcare accelerates toward value-based care, the fast adoption of the Metaverse among healthcare providers remains a true possibility in the coming years. But before implementation, it is essential for healthcare providers to understand what patients require and how this innovative technology can serve an unmet need [ 35 ].

Conclusions

It is difficult to test Metaverse programs because they are still in the experimental stage (determining and recording if a system regularly generates outcomes that satisfy the predetermined criteria). Hence, access to the Metaverse should be expanded rather than restricted. There are risks, but there are also a lot of opportunities. Using the technologically literate young population to take responsibility for their health care and be rewarded for learning, following wellness, and educating their peer groups in a safer social virtual place is a compelling proposal. Pioneers are transforming health education into engaging mini-courses that can be taught online to anybody, anywhere. The prospects for clinicians cooperating throughout the world and aided by AR give the opportunities to address professional health shortages. The opportunity to compensate the community, patients, and professionals for their endeavors to enhance their health opens up a completely new market and income opportunities. Stakeholders in the medical and health industry, such as doctors, patients, ordinary people, government decision-makers, and others, will benefit from the health Metaverse. The health Metaverse application can promote innovative medical education, surgery, medical treatment, and online health management.

This is a new universe that is evolving on a daily basis, and our understanding is expanding along with the pioneers and reformers who are creating these new Metaverses. It is possible to develop a sustainable and economical healthcare paradigm, and healthcare executives must be involved in the process. It is time to take a chance and see what possibilities exist.

The content published in Cureus is the result of clinical experience and/or research by independent individuals or organizations. Cureus is not responsible for the scientific accuracy or reliability of data or conclusions published herein. All content published within Cureus is intended only for educational, research and reference purposes. Additionally, articles published within Cureus should not be deemed a suitable substitute for the advice of a qualified health care professional. Do not disregard or avoid professional medical advice due to content published within Cureus.

The authors have declared that no competing interests exist.

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Top prizes awarded for augmented reality research at ITU Kaleidoscope

By ITU News 

Researchers from The Netherlands, India, Japan, Germany, and South Africa have received the top three awards at this year’s Kaleidoscope academic conference for their research on augmented reality (AR).  

The research looks at land registries, pedestrian navigation, and AR application design, showing how AR can enhance our relationship with our natural and built environment.   

Kaleidoscope  is a peer-reviewed academic conference highlighting research into key topics for the technical standardization work of the International Telecommunication Union (ITU). The 14 th edition of the conference took place last week in Accra, Ghana, hosted by Ghana’s National Communications Authority.  

The conference looked at opportunities in extended reality and how standards could support interoperability and a high-quality user experience — two requirements for successful interaction between different worlds, physical or virtual.  

See the conference programme for all Kaleidoscope presentations and revisit the recorded talks on the conference website .  

All papers accepted and presented at the conference are published in the Kaleidoscope 2022 Proceedings and the  IEEE  Xplore  Digital Library . Outstanding papers may also be published in the ITU Journal on Future and Evolving Technologies and  IEEE Communications Standards Magazine . 

Best papers this year 

Authors of the three best papers — as determined by an international jury of technical experts — receive a share in a prize fund of 6,000 Swiss francs (CHF).

Shared 1 st prize : “The knowledge graph as the interoperability foundation for an Augmented Reality application: The case at the Dutch Land Registry” ( presentation ): Alexandra Rowland and Erwin J.A. Folmer (University of Twente & Kadaster, The Netherlands); Tony Baving (Kadaster, The Netherlands) 

Shared 1 st prize : “ Enhancing user experience in pedestrian navigation based on Augmented Reality and landmark recognition” ( presentation ) by Dhananjay Kumar, Shreayaas Iyer, Easwar Raja and Ragul Kumar (Anna University, MIT Campus, Chennai, India); and Ved P. Kafle (National Institute of Information and Communications Technology, Japan) 

3 rd prize : “ A framework for the design, implementation and evaluation of a multi-variant Augmented Reality application” ( presentation ) by Sophie Westfahl (University of Applied Sciences Neu-Ulm, Germany); Dany Meyer-Renner (University of Applied Sciences Neu-Ulm, Germany); and Antoine Bagula (University of the Western Cape, South Africa). 

Six authors of presented Kaleidoscope papers also received Young Author Recognition Certificates, reserved  for authors under 30 years of age. 

Metaverse insights and more 

Alongside presentations of accepted papers, Kaleidoscope 2022 featured a series of keynote and invited talks as well as an exhibit put on by students at Ghana’s Kwame Nkrumah University of Science and Technology and Ghana Communication Technology University. 

Forward-looking keynotes and invited papers explored the technical demands of metaverse interoperability, high-quality user experience in extended reality, and holographic communications.  

They also presented a clinical evaluation of deep learning systems for the diagnosis of pleural effusion and cardiomegaly in Ghana, Vietnam, and the United States. 

An invited session organized by the conference’s host committee and delivered by the Ghana Communication Technology University looked at how the metaverse could enhance education. 

United for Smart Sustainable Cities (U4SSC) – an initiative supported by ITU and another 17 UN partners – held a ceremony to celebrate Kyebi, in southeastern Ghana, the latest city to implement the U4SSC Key Performance Indicators for Smart Sustainable Cities based on ITU standards.  

Opportunities for research communities 

The  ITU Journal  and  ITU Academia membership  form two more key avenues for academics to engage in ITU’s work. 

The  ITU Journal on Future and Evolving Technologies  provides comprehensive coverage of communications and networking paradigms. Free of charge for both readers and authors, the journal welcomes papers all year, on all topics. ​​​​​​​​​ 

A  webinar series  presented as part of the ITU Journal features internationally renowned researchers. 

ITU Academia members participate in  ITU expert groups  responsible for radiocommunication, standardization and development, strengthening the work of ITU and boosting the impact of their own research. 

Learn more about ITU Academia membership .  

ITU Kaleidoscope 2022 was organized with the technical co-sponsorship of IEEE  and the  IEEE Communications Society and in partnership with  Waseda University , the  Institute of Image Electronics Engineers of Japan , the  Institute of Electronics, Information and Communication Engineers of Japan , the  Chair of Communication and Distributed Systems at RWTH Aachen University , the  European Academy for Standardization , the  University of the Basque Country ,  Liverpool John Moores University , the  University of Limerick , the  Korea Advanced Institute of Science and Technology , and  Confirm Smart Manufacturing .  

Image credit: ITU

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