DOI: 10.3303/CET23107063 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Paper Received: 13 April 2023; Revised: 02 May 2023; Accepted: 12 June 2023 
Please cite this article as: Szabó-Szentgróti E., Konczos-Szombathelyi M., Rámháp S., Kézai P.K., 2023, How Consumers Accept Unmanned 
Smart Stores? – Introducing a Proposed Technology Acceptance Model, Chemical Engineering Transactions, 107, 373-378  
DOI:10.3303/CET23107063 
  

 CHEMICAL ENGINEERING TRANSACTIONS  
 

VOL. 107, 2023 

A publication of 

 

The Italian Association 
of Chemical Engineering 
Online at www.cetjournal.it 

Guest Editors: Petar S. Varbanov, Bohong Wang, Petro Kapustenko 

Copyright © 2023, AIDIC Servizi S.r.l. 

ISBN 979-12-81206-07-6; ISSN 2283-9216 

How Consumers Accept Unmanned Smart Stores? – 

Introducing a Proposed Technology Acceptance Model 

Eszter Szabó-Szentgróti*, Márta Konczosné Szombathelyi, Szabolcs Rámháp, 

Petra Kinga Kézai 

Széchenyi István University Faculty of Economics, Department of Leadership and Marketing, 9024 Győr; Egyetem square 

1., Hungary 

 szabo-szentgroti.eszter@ga.sze.hu 

Digitalization and technological innovation have revolutionized the retail sector. In recent years, a new trend has 

emerged in the form of unmanned stores, pioneered by Amazon Go. Unmanned solutions using artificial 

intelligence are beginning to enter the public consciousness and represent a new sustainability perspective 

(such as lowering paper waste, packaging or using sustainable construction materials) in trade. Although it is 

not yet widespread and is still a new solution for consumers, the global market dynamics suggest that it will 

expand in the future. Unmanned shops pose some challenges, but these can be effectively addressed by the 

appropriate introduction of new technology. To identify or filter out potential shortcomings of this technology on 

the consumer side, it is also necessary to examine the acceptance of this technology by customers. In this 

paper, the internationally accepted Extended Unified Theory of Acceptance and Use of Technology (UTAUT2) 

model was modified and used to examine how consumers accept this technology. For data analysis, Partial 

Least Squares-Structural Equation Modelling method was applied. In the proposed model six constructs were 

examined on how they influence the intention to use. In the performed query, Hungarian university students’ 

behavioural intention is influenced by performance expectancy, effort expectancy, and hedonic motivation. 

However social influence, atmosphere, and price sensitivity have no significant influence on use intention. 

1. Introduction 

Due to rapid economic growth and urbanization, various problems appeared in the last decades in metropolitan 

areas (Ku et al., 2021). The emergence of the global pandemic has resulted in avoiding human contact of 

paramount importance to customers, so e-commerce and the number of consumers using online shopping 

considerably increased (Gazzola et al., 2022). Most countries are experiencing a transition to a “cashless” 

society (Maixé-Altés and Mourelle, 2023). Nowadays in a rapidly changing environment (Bartosova et al. 2021), 

organizations are rapidly changing their structures, systems, work processes and activities. This changing 

environment calls for managers to manage and respond to the changes in an appropriate manner (Yacob et al. 

2018). The early adopters are the small and medium enterprises (SMEs) that see the present emerging 

economies and disruptive technology as an opportunity to increase their productivity and competitiveness (Kuok 

and Promentilla, 2021). One recent sustainable innovation is the cashierless concept, which is when a store is 

completely automated and human interaction is very limited (Schögel and Lienhard, 2020). The concept, which 

is a combination of artificial intelligence, computer vision, deep learning and edge computing, allows end users 

to enter the store and exit as quickly and with as little human contact as possible (Falcão et al. 2020). The 

shopping process is as follows: download an application; register, then, after the registration process, the 

consumer is free to simply buy products and exit the store without needing to “check out” in the traditional sense 

(Ponte and Bonazzi, 2021). Amazon, the world-famous online retailer founded by American Jeff Bezos, invented 

the concept to eliminate queuing in stores (Gross, 2019). On the 22nd of January 2018 after 5 y of development, 

Amazon opened the first self-service Amazon Go store with the so-called “Just Walk Out” technology (Türegün, 

2019). Since then, several startups have developed similar technology, e.g., AIFI, Trigo, Bingobox, and 

Cloudpick (Szabó-Szentgróti et al., 2023). Although this technology has been available globally and sporadically 

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for a few years, it is not yet widespread. It is important to examine how consumers react to such an innovative 

technology. The main purpose of this study is to analyse how university students accept this sustainable 

innovation concept of cashierless stores in Hungary. In this study, we use the terminology of cashierless store 

and unmanned store as synonyms. The rest of the paper is structured as follows: Section 2 – Methodology; 

Section 3 – Model assessment and hypothesis results; Section 4 – Conclusion and future works. 

2. Methodology 

2.1 Data collection and measures 

The research is based on an online survey of 246 Hungarian university students. The questionnaire was 

designed to reveal the technology acceptance of unmanned stores using validated scales (Appendix A). Unified 

Theory of Acceptance and Use of Technology Model (UTAUT2) ensured the basis of our proposed model. The 

original UTAUT2 (Venkatesh et al., 2012) model had to be adapted to the objectives of this research. Concerning 

the model development Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, 

Hedonic Motivation constructs were applied from Venkatesh et al. (2012). A new construct was created called 

Atmosphere, where the goal was to find out whether Behavioural Intention is influenced by the special indoor 

atmosphere of these shops. Technology penetration does not yet allow respondents to judge value for money, 

thus Price Value construct has been replaced by Price Sensitivity. Furthermore, Hungarian consumers are 

mostly price sensitive (Garai-Fodor et al., 2022) and these shops represent high price levels. Concerning 

Behavioural Intention construct statements, a few drafting corrections were needed to make them meaningful 

for the analysed technology. Use Behaviour variable was excluded from our model, because there is no 

everyday use for such shops in Hungary. For each of the 8 constructs, respondents rated 29 statements on a 

Likert scale of 1 to 7, where 1 was ‘strongly disagree’ and 7 was ‘strongly agree’. Answers were collected to 

measure more expectations rather than experience-based opinions, due to the limited availability of these 

shops. The survey was conducted from February to May 2023 to collect as many answers as possible. In order 

to avoid misunderstanding, the questionnaire started with the definition of unmanned stores. Participants were 

informed about the anonymity and voluntarity of the survey, and they could stop giving answers without giving 

a reason. From 269 respondents 23 were excluded from the analysed database due to inconsistent answers, 

thus the final sample size was 246. Our respondents’ ages ranged from 18 to 39 y including full-time and part-

time university students. The sample’s distribution between male and female respondents was 69.1 % and 

30.9 %; 6.9 % live in the capital city, 43.1 % are residents of a county rank town while 22.8 % live in other towns, 

and 27.2 % live in villages. The results were analysed with SmartPLS version 4.0.9.3.  

2.2 PLS-SEM 

This study utilized the PLS-SEM (partial least squares-structural equation modeling) method, a widely used 

statistical tool by researchers in many scientific fields (Lowry and Gaskin, 2014). According to Hair et al. (2011) 

PLS-SEM consists of a measurement model and a structural model at the same time, where the measurement 

(outer) model describes the indirect relationships between the observed (indicator) and latent variables. The 

structural (inner) model shows the relationships (paths) between the latent constructs. PLS-SEM method can 

deal with both reflective and formative indicators simultaneously. Hypothesis formulation (Table 1 and Figure 1) 

was adapted to the structure of the model of how the examined latent variables influence Behavioural Intention.  

Table 1: Model Hypothesises for cashierless intelligent stores 

Hypothesises 

H1: Performance Expectancy (PE) directly and positively influences Behavioural Intention (BI). 

H2: Effort Expectancy (EE) directly and positively influences Behavioural Intention (BI). 

H3: Social Influence (SI) directly and positively influences Behavioural Intention (BI). 

H4: Facilitating Conditions (FC) directly and positively influences Behavioural Intention (BI). 

H5: Hedonic Motivation (HM) directly and positively influences Behavioural Intention (BI). 

H6: Atmosphere (AT) directly and positively influences Behavioural Intention (BI). 

H7: Price Sensitivity (PS) directly and negatively influences Behavioural Intention (BI). 

3. Results and discussion 

3.1 Measurement model 

Reflective measurement model was analysed in regard to its reliability using outer loading values, average 

variance extracted (AVE), Cronbach’s alpha (α Value), and composite reliability (CR). Due to poor construct 

reliability, some statements and Facilitating Conditions (FC) were excluded from the final model (excluded 

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variables see Appendix Table A1). It is shown in Table 2 that outer loadings are all greater than 0.7 and AVE 

values of the latent constructs are all above the limit value of 0.5 (Hair et al., 2010). Cronbach’s alpha is another 

measure of internal consistency reliability with a threshold >0.7. Only Atmosphere has an α value of 0.610, but 

concerning the view of Boudreau et al. (2001), Cronbach’s alpha is a less precise measure of reliability, and 

Griethuijsen et al. (2014) allows a 0.6 threshold value. As other measures are appropriate in the case of the 

Atmosphere latent variable, no exclusion was necessary. According to Hair et al. (2011), CR values of 0.60 to 

0.70 in exploratory research and values from 0.70 to 0.90 in more advanced stages of research are considered 

to be satisfactory. All CR values in Table 2 are above that threshold. Variables that did not meet the thresholds 

were excluded (Appendix A) from the final model. 

Table 2: Construct reliability, convergent validity and VIF values, own data generated by PLS-SEM analysis 

Constructs Items Outer loadings P values AVE α Value CR VIF 

Performance 

Expectancy 

PE2 0.877 0.000 

0.739 0.824 0.895 

2.024 

PE3 0.840 0.000 1.758 

PE4 0.862 0.000 1.848 

Effort Expectancy 

EE1 0.782 0.000 

0.657 0.832 0.884 

1.965 

EE2 0.855 0.000 1.957 

EE3 0.854 0.000 1.774 

EE4 0.746 0.000 1.857 

Social Influence 

SI1 0.872 0.000 

0.784 0.862 0.916 

2.013 

SI2 0.886 0.000 2.390 

SI3 0.898 0.000 2.302 

Hedonic Motivation 

HM1 0.911 0.000 

0.840 0.905 0.940 

2.987 

HM2 0.918 0.000 2.704 

HM3 0.920 0.000 3.120 

Atmosphere 
AT1 0.859 0.000 

0.719 0.610 0.837 
1.239 

AT3 0.837 0.000 1.239 

Price Sensitivity 

PS2 0.885 0.000 

0.759 0.841 0.904 

2.198 

PS3 0.894 0.000 2.516 

PS4 0.834 0.000 1.720 

Behavioural Intention 

BI1 0.891 0.000 

0.770 0.851 0.909 

2.285 

BI2 0.819 0.000 1.807 

BI3 0.921 0.000 2.784 

Discriminant validity shows that constructs used in the model are distinct from one another (Hair et al., 2017) 

where Heterotrait-Monotrait Ratio (HTMT) and Fornell-Larker criteria are widely used in research to confirm that. 

Henseler et al. (2015) pointed out that Fornell-Larker criteria perform poorly and HTMT is recommended instead. 

In the present study, all HTMT values are below 0.9 (Table 3) confirming discriminant validity of our model. 

Table 3: Discriminant Validity (HTMT criteria), own data generated by PLS-SEM analysis 

               AT BI EE HM PE PS SI 

AT        

BI 0.666       

EE 0.812 0.673      

HM 0.710 0.823 0.614     

PE 0.622 0.897 0.640 0.736    

PS 0.117 0.409 0.160 0.344 0.386   

SI 0.231 0.552 0.357 0.491 0.538 0.474  

A multicollinearity test was carried out before estimating the structural model, for which VIF (Variance Inflation 

Factor) values were tested. VIF values above 5.0 indicate multicollinearity, according to Hair et al. (2011); 

therefore, VIF values in Table 2 show no multicollinearity between latent constructs. 

3.2 Structural model and hypothesis testing 

The structural model assessment was performed by 5000 bootstrap calculations, during which the statistical 

significance of the path coefficient was carried out, allowing hypotheses to be tested. To examine model fitness 

Standardized Root Mean Square (SRMR) was used and must be less than 0.08 according to Henseler et al. 

(2016). This model shows an adequate level of model fitness with an SRMR value of 0.073, which indicates a 

good fit. The final proposed model is seen in Figure 1. 

375



 

Figure 1: Proposed model of cashierless store technology acceptance. Source: own data generated by PLS-

SEM analysis 

R2 was evaluated where the final model has 0.695 adjusted R2 value, which suggests that a 69.5% variance in 

Behavioural Intention of cashierless stores can be explained by the analysed six latent variables. Accordingly, 

the structural model was considered to be moderate (0.5 < r < 0.75) (Hair et al., 2011) and 0.695 value means 

a sufficient explanatory power. Predictive relevance of the model was established by Q2
predict value of 0.683 that 

indicates good predictive accuracy. Relationships of the research model were analysed to test six hypothesises 

(due to poor construct reliability Facilitating Conditions [FC] was excluded [H4] from hypothesis testing). Results 

show in Table 4 that Performance Expectancy (ß=0.383, p=0.000), Effort Expectancy (ß=0.148, p=0.000) and 

Hedonic Motivation (ß=0.319, p=0.000) significantly influenced Behavioural Intention (BI), while Social Influence 

(ß=0.070, p=0.118), Atmosphere (ß=0.059, p=0.243) and Price Sensitivity (ß=0.079, p=0.066) have no 

significant effect on the BI variable. 

Table 4: Bootstrap results and hypothesis results. Source: own data generated by PLS-SEM analysis 

 

 
Original sample 

Sample 

mean 
Standard deviation T statistics P values 

Hypothesis 

validation 

PE -> BI (H1) 0.383 0.386 0.060 6.395 0.000* supported 

EE -> BI (H2) 0.148 0.148 0.048 3.078 0.002* supported 

SI -> BI (H3) 0.070 0.071 0.045 1.564 0.118 not supported 

HM -> BI (H5) 0.319 0.317 0.059 5.433 0.000* supported 

AT -> BI (H6) 0.059 0.059 0.051 1.167 0.243 not supported 

PS -> BI (H7) 0.079 0.078 0.043 1.839 0.066 not supported 

4. Conclusions 

The purpose of this study was to assess which factors influence Behavioural Intention in the case of cashierless 

stores among Hungarian university students. Research in this area is still scarce in Hungary and there has been 

less research on it internationally, as well. The proposed model was based on the UTAUT2 method, combining 

validated scales that have been adapted to the research.  

There are several conclusions that can be drawn from the result. Performance Expectancy significantly 

influences Behavioural Intention (H1), which means that according to the respondents shopping in cashierless 

stores it would help them to be more productive and finish shopping more quickly. As some technological skills 

and smartphone usage are required to shop at unmanned stores, H2 results show that respondents can easily 

cope with using this new technology. Social Influence has no significant effect on Behavioural Intention (H3), 

which indicates that respondents are not influenced by other people’s opinions to do their shopping in such 

stores. Unmanned stores provide a different shopping experience for consumers and our results show that 

respondents would find cashierless shopping fun, enjoyable and entertaining (H5). The Atmosphere variable 

was created to measure how current minimalist interior space of these shops influences Behavioural Intention 

(H6), but no significant effect was experienced, which means respondents would not require a minimalist interior. 

In the current phase of this technology, products have higher prices than in traditional brick-and-mortar shops. 

According to our results, Price Sensitivity has no significant effect on Behavioural Intention among Hungarian 

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university students (H7), which is a surprising result in that a significant proportion of Hungarian consumers are 

considered price sensitive (Garai-Fodor et al., 2022). In summary this paper investigated which factors affect 

the intention to use cashierless stores among Hungarian university students. Three factors were identified which 

determine Behavioural Intention. From a practical perspective, results support retailers in their market entry 

strategic decisions because university students could be an important target group of cashierless stores.  

Limitations and future research possibilities should be outlined. Results show the current opinion of the 

respondents; moreover, as only one cashierless store is available in Hungary, results will probably vary, as this 

technology will spread. The results cannot be generalized to the whole population in Hungary; the results are 

valid only for Hungarian university respondents. Thus, in the future, a wider Hungarian sample needs to be 

analysed, which is already in progress. An international perspective is also planned to examine where more 

countries will be involved to ensure international comparisons.  

Appendix A 

Table A1: Research statements 

Code CONSTRUCTS AND STATEMENTS 

PE1 I would find automated smart stores useful in my daily life (Venkatesh et al., 2012); deleted 

PE2 Using automated smart stores would increase my flexibility daily (Kapser and Abdelrahman, 2020) 

PE3 Using automated smart stores would help me accomplish things more quickly (Venkatesh et al., 2012) 

PE4 Using automated smart stores would increase my productivity (Venkatesh et al., 2012) 

EE1 Learning how to shop in automated smart stores would be easy for me (Venkatesh et al., 2012) 

EE2 My interaction with automated smart stores would be clear and understandable (Venkatesh et al., 2012) 

EE3 I would find automated smart stores easy to use (Venkatesh et al., 2012)  

EE4 It would be easy for me to become skilful at shopping in automated smart stores. (Venkatesh et al., 2012) 

SI1 People who are important to me expect me to shop at automated smart stores (Venkatesh et al., 2012) 

SI2 People who influence my behaviour expect me to shop at automated smart stores (Venkatesh et al., 2012) 

SI3 People whose opinions I value would prefer that I shop at automated smart stores (Venkatesh et al., 2012) 

FC1 I have the resources necessary to shop at automated smart stores (Venkatesh et al., 2012); deleted 

FC2 I have the knowledge necessary to shop at automated smart stores (Venkatesh et al., 2012); deleted 

FC3 Automated smart stores are compatible with other technologies I use (Venkatesh et al., 2012); deleted 

FC4 I can get help from others when shopping at automated smart stores (Venkatesh et al., 2012); deleted 

HM1 Shopping at automated smart stores would be fun (Venkatesh et al., 2012) 

HM2 Shopping at automated smart stores would be enjoyable (Venkatesh et al., 2012) 

HM3 Shopping at automated smart stores would be very entertaining (Venkatesh et al., 2012) 

AT1 I would prefer automated smart stores to be less crowded with customers (own statement) 

AT2 I would prefer automated smart stores to have smaller selection of products (own statement; deleted) 

AT3 I would prefer automated smart stores to have clean and simple interior (own statement) 

AT4 I would find products easier in automated smart stores (own statement; deleted) 

PS1 Automated smart stores would offer me better value for money (Indrawati and Putri, 2018); deleted 

PS2 I would not mind paying more to try automated smart stores (Kapser and Abdelrahman, 2020) 

PS3 I would not mind spending more to get my shopping done in automated smart stores. (Kapser and 

Abdelrahman, 2020) 

PS4 If I knew that automated smart stores were likely to be more expensive than conventional shopping options 

that would not matter to me. (Kapser and Abdelrahman, 2020) 

BI1 I intend to shop at automated smart stores in the future. (Venkatesh et al., 2012) 

BI2 I would always try to shop at automated smart stores in my daily life. (Venkatesh et al., 2012) 

BI3 
I plan to shop at automated smart stores frequently when available in the future. (Kapser and Abdelrahman, 

2020) 

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