Customer acceptance of cashless payment systems in the

hospitality industry Ahmet Bulent Ozturk

Rosen College of Hospitality Management, University of Central Florida, Orlando, Florida, USA

Abstract Purpose – The purpose of this study is to propose and test an extended version of technology acceptance model (TAM) to examine consumers’ acceptance of radio frequency identification (RFID) cashless payment systems in the hospitality industry. Design/methodology/approach – A comprehensive structural model was developed by adding two additional constructs to original TAM, namely, self-efficacy and perceived risk. A self-administered online questionnaire was used to collect the data of the study from 305 respondents in the USA. Confirmatory factor analysis was conducted to validate the measurement model and structural equation modeling analysis was performed to test the proposed model. Findings – Study results indicated that self-efficacy was significantly related to perceived ease of use. In addition, perceived risk significantly negatively influenced perceived usefulness and perceived ease of use. Study results further illustrated that perceived ease of use had a significant impact on perceived usefulness and both perceived ease of use and perceived usefulness were significantly associated with intention to use. Practical implications – Study findings provide significant practical implications for US hospitality operators and technology vendors for identifying factors affecting users’ acceptance of RFID cashless payment systems in the hospitality industry. Originality/value – By extending TAM, this study is one of the first studies to investigate RFID cashless payment system acceptance in the hospitality industry.

Keywords Technology acceptance model, RFID, Self-efficacy, Perceived risk, Cashless payment

Paper type Research paper

Introduction Over the past decade, radio frequency identification (RFID) technology has received great deal of attention because of its potential to improve organizational performance and enable new business models (Smith et al., 2014). In general, RFID technology can be defined as an automated data collection technology that transmits different types of data wirelessly between a RFID tag and a reader device using radio waves (Want, 2004). A basic RFID system is made up of three components including RFID transponder (or tag), RFID reader and back office data processing equipment. Each tag contains unique identification number and electronically sorted information about the product (e.g. product attributes, physical dimensions and price) to which it is embedded and transmits that data to the reader through radio waves. The RFID reader receives radio waves to read the identification number of the tag and the information stored in the tag. Finally, the reader transfers

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Received 22 February 2015 Revised 3 July 2015

28 October 2015 Accepted 11 November 2015

International Journal of Contemporary Hospitality

Management Vol. 28 No. 4, 2016

pp. 801-817 © Emerald Group Publishing Limited

0959-6119 DOI 10.1108/IJCHM-02-2015-0073

the readings to one or more back office data processing equipment which in turn process the collected data (Wu et al., 2006; Zhu et al., 2012).

Even though RFID technology falls under the category of automatic identification technologies such as bar codes and optical character readers, there are important differences among these technologies. For instance, unlike barcode technology, RFID does not require line of sight, as RFID tags can be read as long as they are within the range of the RFID reader. In addition, as each tag has a unique identification number, the readers can differentiate among numerous tags that are within the range of the RFID reader and communicate with multiple tags simultaneously (Violino, 2005; Hoang and Caudill, 2006).

The commercial use RFID technology has been initially started by retail companies (e.g. WalMart) to improve efficiency in the supply chain. However, various types of RFID technologies have been used in different contexts in recent years. Some of the applications of RFID technology include inventory tracking, building access control, toll collection and tracking library books (Ozturk et al., 2012). On the other hand, with the decreased cost of equipment and tags, more customer-facing applications have emerged especially in the hospitality industry. One such technology, namely, RFID-based cashless payment system, has become popular in the hospitality industry in recent years. Thanks to its great cost-saving and revenue-generating benefits for operators and its convenience and ease of use for customers, a growing number of hospitality firms have already adopted or planning to adopt this technology to improve their service effectiveness to the customers (Ozturk and Hancer, 2014).

However, RFID technology in general and RFID cashless payment systems in particular are progressing at a very fast pace, which creates uncertainties about both the benefits and the risks associated with it (Ferrer et al., 2010). Therefore, consumers may be hesitant to use RFID cashless payment systems, as the risk they perceive may be overwhelming compared other traditional ways of payments (i.e. cash, credit/debit card and checks) because of uncertainties and potential undesirable outcomes. On the other hand, RFID cashless payment systems may be perceived as a complicated technology where users’ judgments about their capabilities (i.e. required knowledge, skills and self-efficacy) to use the technology may influence their acceptance. For these reasons, there is a need for a better understanding of the factors affecting consumers’ acceptance of RFID cashless payment systems. However, although numerous types of RFID applications have been extensively investigated in retail, healthcare and logistic, there has been little or no research that examined the acceptance of RFID cashless payment systems in the hospitality industry.

Based on the preceding discussion, the purpose of this study was to develop and test a comprehensive conceptual model that examined consumers’ acceptance of RFID cashless payment systems in the hospitality industry. To this end, an extended version of technology acceptance model (TAM) (Davis, 1989) was used to test the proposed model. In addition to perceived ease of use and perceived usefulness, TAM is extended by adding two constructs to: perceived self-efficacy and perceived risk. Particulary, this research analyzed the influence of self efficacy on perceived usefulness and perceived ease of use and the impact of perceived risk on perceived usefulness and intention to use.

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This study further investigated the relationships between perceived usefulness and perceived ease of use and behavioral intention to use.

Review of literature RFID cashless payment systems A RFID cashless payment system can be defined as a system that allows consumers to set up an account linked to an RFID device (e.g. wristband, card, key chain or hotel room key) that can be used to make purchases by simply waving it over a RFID-enabled POS workstations at any location that supports RFID cashless payment (Muta, 2006). Currently, different types of RFID cashless payment systems are available in the market which differ in their technical characteristics and application areas. However, one of the main and common ways to categorize RFID cashless payment systems is based on the volume of acceptance points offered to an end-user. From this perspective, RFID cashless payment systems can be grouped under two broad categories: open-loop systems and closed-loop systems.

An open-loop RFID system link a payment device (e.g. a card embedded with RFID chip) directly to consumers’ credit or debit card (alternatively, consumers can set up a prepaid account which they refill by mail, online or at select merchant locations.) To make purchases, consumers simply wave their RFID cards over a scanner at any retailer that supports RFID cashless payment (BTD International Consulting, 2012). Octopus card which was launched back in 1997 in Hong Kong is a good example of an open-loop RFID system. Especially after the scope of its transactions widened in 2000, consumers can now use Octopus card not only for local transportation such as to pay a trip on a ferry, train or bus, but also for purchases at convenience stores, supermarkets and fast-food restaurants (Lok, 2004).

In contrast to open-loop system, closed-loop RFID systems are designed to accept cashless payments exclusively at the respective property (e.g. hotels and theme parks) or venue. (BTD International Consulting, 2012). At registration, consumers (guests) set up an account which is linked to a payment device (e.g. wristband or room key with RFID chip on it) that can be used to make purchases by waving it over a RFID-enabled point of sale (POS) workstations anywhere within the property (Ozturk et al., 2012). In hotels, for instance, guests are provided with a RFID-enabled room key or a wristband at registration and have the choice of using these devices to make the charges go directly to their rooms (Rock, 2007). A good example of closed-loop RFID cashless payment systems is the MyMagic� program by The Walt Disney Company. Worn on the wrist, a MagicBand, is a RFID-enabled wristband that can be read by short-range readers located throughout the Walt Disney World Resort. MagicBands are linked to guests’ “My Disney Experience” account and contains all vacation related information including hotel and restaurant reservations and Fastpass� experiences and more. Guest can also use their MagicBands as their Disney resort room key (Swedberg, 2014). However, maybe the biggest convenience of MagicBands is coming from its ability to be used for purchases throughout the Walt Disney World theme parks. As guests are wearing these wristbands, they do not need to carry cash or their credit cards with them all the time reducing the risk of losing them significantly. By simply waving the MagicBands over a reader near the cash register and entering their personal identification number, guests can complete their purchases in a few seconds (Swedberg, 2014).

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Theoretical framework and research model TAM (Davis, 1989) was adopted to identify factors affecting customers’ acceptance of RFID cashless payment systems in the hospitality industry. Davis (1989) stated that there are numerous variables, which impact users to accept or reject a technology. Among those variables, previous studies suggested that two determinants are especially important. First, “people tend to use or not use an application to the extent they believe it will help them perform their job better” (Davis, 1989, p. 320). Davis (1989) refers to this first variable as “perceived usefulness”. Second, people perceive that if the technology is too hard to use, they tend not to adopt and not to use it even though they believe that the technology is useful. Davis (1989) refers to this second variable as “perceived ease of use”.

There is an extensive research in the information technology literature that validated the effectiveness of TAM in predicting individuals’ intention to use an innovation (Kim and Qu, 2014; Morosan, 2014). In addition to the many studies that have adopted TAM as a research model, there are also several studies which extended the model with other constructs such as compatibility (Kim and Qu, 2014), self-efficacy (Hernandez et al., 2009), trust and perceived risk (Kesharwani and Bisht, 2012) and perceived security (Hossain and Prybutok, 2008).

TAM is originally developed for employees’ technology acceptance in organizations. Even though many TAM studies have investigated information technology (IT) acceptance in the context of work-related activities, the theory is applicable to diverse non-organizational settings (Thiesse, 2007). However, for the RFID cashless payment technology, some of the individual difference and usage-context factors may be more critical compared to other types of technologies. This may change the original TAM model for using it in explaining the users’ acceptance of RFID cashless payment systems. Therefore, TAM requires extension to account for additional constructs that are suggested in the RFID literature (Hossain and Prybutok, 2008). Therefore, to identify customers’ acceptance of RFID cashless payment systems, an extended version of TAM was used in this study. In addition to perceived ease of use and perceived usefulness, TAM is extended by adding two constructs to it: self-efficacy and perceived risk (Figure 1).

Self-Efficacy

Perceived Risk

Perceived Ease of Use

Perceived Usefulness

Inten�on to Use

H2

H1

H3

H4

H5

H6

H7

Figure 1. Conceptual framework and hypotheses

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Self-efficacy, perceived ease of use and perceived usefulness A key concept in Bandura’s (1986) social cognitive theory, self-efficacy, refers to:

[…] people’s judgments about their capabilities to organize and execute courses of action necessary to perform a given task and it is concerned not with the skills one has but with judgments of what one can do with whatever skills one possesses (Bandura, 1986, p. 391).

Self-efficacy affects what behaviors people choose to perform, the amount of effort they are ready to use and the amount of time they will persist to overcome obstacles (Bandura, 1986). Based on Luarn and Lin’s (2005) mobile banking study, this study has focused on whether individuals believed that they had the necessary knowledge, skill or ability to use RFID cashless payment systems. Therefore, perceived self-efficacy was defined as the judgment of one’s ability to use RFID cashless payment systems.

Self-efficacy affects an individual’s system anxiety which in turn affects the perceived ease of use and perceived usefulness of the system (Igbaria and Iivari, 1995). Prior research in the context of information systems, suggests a direct relationship between self-efficacy and perceived usefulness and perceived ease of use (Hasan, 2007; Wang et al., 2003). For instance, by integrating self-efficacy as an external variable to the TAM, Hasan (2007) assessed the direct effect of self-efficacy on perceived eased of use and perceived usefulness. Data collected from 121 respondents demonstrated that self-efficacy significantly influenced perceived usefulness and perceived ease of use. Another study conducted by Jashapara and Tai (2011) found a significant positive relationship between self-efficacy and perceived ease of use. In the context of RFID technology, Chong and Chan (2012) stated that:

[…] if a person has high confidence on his or her ability to use RFID, it can serve as a basis for the person’s perceptions of how easy RFID will be to use (p. 111).

Given the strong empirical support for the relationship between self-efficacy and perceived ease of use and perceived usefulness, the following hypotheses were proposed:

H1. There is a positive relationship between self-efficacy and perceived ease of use.

H2. There is a positive relationship between self-efficacy and perceived usefulness.

Perceived risk, perceived usefulness and intention to use In the context of IS, perceived risk has been defined in different ways. According to the “theory of perceived risk”, consumers perceive risk because they face uncertainty and potentially undesirable consequences because of purchases (Lim, 2003). According to Bauer (1960):

[…] consumers behavior involves risk in the sense that any action of a consumer will produce consequences which he/she cannot anticipate with anything approximating certainty, and some of which at least are likely to be unpleasant (p. 24).

Prior research suggested that perceived risk is an important factor for consumers’ acceptance of a technology. Researchers have focused on the relationship between perceived risk and behavioral intention to use in various contexts including electronic commerce (Pavlou, 2003; Lim, 2003), self-service technologies (Kim and Qu, 2014), mobile commerce (Zhang et al., 2012) and mobile banking (Chen, 2013).

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As in all other technologies, there are certain risks involve in using RFID cashless payment systems. As previously discussed RFID technologies mainly rely on wireless transmission between the tags and the readers which creates a risk of interference between RFID and other technologies in the work place (Core, 2009). In addition, the performance of RFID tags deteriorates over time. This problem reduces the read range and consequently the tag stops working completely causing the whole system fail.

In this context, such risks associated with RFID cashless payment system may influence users’ perceived usefulness and behavioral intention. Even though there have been a limited number of studies specifically focusing on RFID cashless payment systems, a few studies investigated the risks associated with similar RFID systems. For example, a recent study conducted by Zhu et al. (2012) examined the role of perceived risk in the adoption of RFID credit cards. By integrating perceived risk to the TAM, their study results indicated that perceived risk was directly and negatively associated with both perceived usefulness and intention to use. Based on the theoretical and empirical support from the literature, the following hypotheses were developed:

H3. There is a negative relationship between perceived risk and perceived usefulness.

H4. There is negative relationship between perceived risk and intention to use.

Perceived ease of use, perceived usefulness and intention to use Perceived usefulness was considered as a motivation to engage with use of information system, whereas perceived ease of use was regarded as an antecedent of perceived usefulness. More specifically, perceived usefulness is defined as “the degree to which a person believes that using a particular system would enhance his/her task performance”. Perceived ease of use, on the other hand, refers to “the degree to which a person believes that using a particular system would be free of effort (Davis, 1989, p. 320)”. Prior research validated the impact of perceived ease of use and perceived usefulness on intention to use in various IT contexts including online booking (Kucukusta et al., 2015), mobile wireless technology (Kim and Garrison, 2009), e-commerce (Hernandez et al., 2009), mobile banking (Gu et al., 2009) and mobile commerce (Chong et al., 2012).

In the current study, perceived usefulness was defined as the extent to which users believes that using RFID cashless payment systems saves them time and enhances the effectiveness of the payment process. On the other hand, perceived ease of use was defined as the extent to which users believe that RFID cashless payment systems do not make the users more confused and they are easy to understand and ease to use. Prior research in the context of RFID technology also confirmed the positive influence of perceived usefulness and perceived ease of use on behavioral intention. For instance, Hossain and Prybutok (2008) contextualized TAM to RFID technology by substituting perceived convenience for perceived usefulness and perceived ease of use. The findings of their study indicated that higher perceived convenience (perceived usefulness and perceived ease of use) leaded to greater acceptance of RFID technology. Another study conducted by Cheng (2013) investigated consumer attitudes and behavioral intention to use an RFID door security system based on TAM. Data collected from 250 consumers of Taipei Arena Ice Land, Taiwan, demonstrated that perceived ease of use had a significant positive impact on perceived usefulness, perceived usefulness and perceived

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ease of use both positively influenced attitudes toward use and perceived usefulness had a significant direct positive impact on behavioral intentions to use. Hence:

H5. There is a significant relationship between perceived usefulness and intention to use.

H6. There is positive relationship between perceived ease of use intention to use.

H7. There is significant relationship between perceived ease of use and perceived usefulness.

Methodology A Web-based questionnaire was developed based on the literature on TAM and RFID technology acceptance. All research constructs were adapted from prior research and minor modifications were applied to the constructs in the context of RFID domain. All items were measured by five-point Likert scale ranging from “strongly disagree” to “strongly agree”. Perceived ease of use and perceived usefulness were measured by four items each, adapted from Davis (1989). Self-efficacy was measured via six items developed by Compeau and Higgins (1995). Perceived risk was measured using four items adapted from the study by Im et al. (2008). Finally, behavioral intention was measured using a three-item scale adapted from the work of Davis et al. (1992). A brief description of RFID cashless payment system was provided at the beginning of the survey to make sure that all participants had a good understanding of the technology.

The data of the study were collected through a 1.5 million frequent traveler database purchased by Oklahoma State University the Center for Hospitality and Tourism Research. The frequent travelers who stayed at a commercial hotel at least once during the previous six months were the target population of the study. A screening question was utilized and participants were excluded from the sample if they had not stayed at a commercial hotel at least once during the previous six months.

Simple random sampling technique was used and every 25th traveler was selected and included to the sample. A Web survey was used to collect the data. Respondents were invited to the survey through email. An email reminder was sent out after 2 week to complete the survey. In all, 60,000 were sent out and 45,000 emails were delivered successfully. Only 462 surveys were returned creating a response rate of 1 per cent. In all, 157 questionnaires were eliminated because of incomplete and invalid responses. A total of 305 questionnaires were used for the data analysis. Two-step approach suggested by Fornell and Larcker (1981) was utilized to test the proposed model. Measurement model was analyzed by a confirmatory factor analysis (CFA) followed by structural equation modeling (SEM) analysis by using AMOS 22.0.

Results Respondents’ profile Table I shows the demographic characteristics of the respondents. About 54 per cent of the respondents were male and 44 per cent of the respondents were female. The majority of the respondents were between 51 and 60 (25 per cent), and 41 and 50 (24 per cent) years old. In all, 40 percent of the respondents stated that they had a bachelor’s degree and 28 per cent of the participants had an associate’s degree. As for the question related to income, 25 per cent of the respondents reported that they had a household income of

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$41,000 and $80,000 per year. Finally, about 70 per cent of the participants stated that they had previous RFID technology experience in the hospitality industry.

Confirmatory factor analysis The measurement model was analyzed by a CFA. To assess the overall model, goodness-of-fit measures were used. The literature suggests that chi square to degrees of freedom ratio should be less than 3 for an acceptable model fit (Hair et al., 2009). The ratio of chi square to degree of freedom was 2.49 �2 � 446.93, df � 179) which demonstrated an acceptable model fit with other fit indexes including RMSEA � 0.07, NFI � 0.93, CFI � 0.96, IFI � 0.96 and RFI � 0.92. Overall, the results were considered

Table I. Respondents’ demographic characteristics and past experience with RFID technology

Demographic characteristics N (%)

Gender Male 166 54.4 Female 135 44.3 Missing 4 1.3 Total 305 100

Age 18-30 48 15.7 31-40 48 15.7 41-50 73 23.9 51-60 76 24.9 61 and older 58 19.0 Missing 2 0.7 Total 305 100

Income $20,000 or less 33 10.8 $21,000-$40,000 48 15.7 $41,000-$60,000 76 24.9 $61,000-$80,000 77 25.2 $81,000 or more 69 22.6 Missing 2 0.7 Total 305 100

Education Below high school 2 0.7 High school 38 12.5 Associate’s degree 85 27.9 Bachelor’s degree 123 40.3 Master’s degree 43 14.1 Doctorate degree 13 4.3 Missing 1 0.3 Total 305 100

Past experience Yes 214 70.2 No 91 29.8 Total 305 100

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appropriate for further analyses. To assess the reliability of the measurement scales, the composite reliability (CR) estimates and Cronbach’s alpha values were calculated. The results indicated that the alpha coefficients values for the scales ranged from 0.71 to 0.91, and the CR values for the scales were above 0.7 indicating a strong reliability (Hair et al., 2009) (Table II). In addition, the average variance extracted (AVE) scores were used to assess convergent validity. The AVE values ranged from 0.72 to 0.88 which exceeded

Table II. Measurement model

results

Factors Standard loadings CR AVE

Self-efficacy (SE) 0.94 0.72 I could use RFID payment systems if someone else had helped me 0.92 I could use RFID payment systems if I could call someone for help when I got stuck 0.89 I could use RFID payment systems if someone showed me how to do it first 0.88 I could use RFID payment systems if I had seen someone else using it before trying it myself 0.86 I could use RFID payment systems if I had just built-in help facility for assistance 0.79 I could use RFID payment systems if I had only software manuals for reference 0.77

Perceived usefulness (PU) 0.96 0.88 I believe payment transactions would be difficult to perform without RFID payment systems 0.95 I believe using RFID payment systems saves me time 0.92 Overall, I find RFID payment systems useful 0.92 I believe using RFID payment systems enhances the effectiveness of the payment process 0.90

Perceived ease of use (PEOU) 0.91 0.73 I believe using RFID payment systems will not make me more confused 0.72 I believe my interaction with RFID payment systems will be easy to understand 0.82 Overall, I believe RFID payment systems are easy to use 0.94 I find it cumbersome to use RFID payment systems 0.94

Perceived risk (PR) 0.93 0.79 RFID payment systems would not frustrate because of its poor performance 0.83 RFID payment systems would be effective as I think 0.89 RFID payment systems would be worth its cost 0.90 Comparing with other technologies, RFID payment systems do not have more uncertainties 0.93

Intention to use 0.93 0.83 Given the chance I intend to use RFID payment systems 0.87 Given the chance I predict that I should use RFID payment systems 0.89 Given the chance I plan to use RFID payment systems 0.93

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the 0.50 cutoff recommended by Fornell and Larcker (1981) (Table II). Finally, discriminant validity was assessed by comparing the square root of AVE of each latent construct with the inter-construct correlations (Fornell and Larcker, 1981). All diagonal values exceeded the inter-construct correlations indicating an acceptable level of discriminate validity (Table III).

Structural equation model The SEM was analyzed to test the hypothesized relationships among the constructs. The goodness-of-fit measures were used to evaluate the structural model fit. Overall the results demonstrated that the fit measures for the study model were reasonable with �2 � 580.308, df � 182, RMSEA � 0.08, CFI � 0.94, PNFI � 0.80, IFI � 0.94 and RFI � 0.90. Furthermore, the variance explained for perceived usefulness was 40 per cent, for perceived ease of use was 29 per cent and for intention to use was 53 per cent. Overall, the results demonstrated that except H2, all of the study hypotheses were supported. More specifically, the results indicated that self-efficacy had a significant positive impact on perceived ease of use and perceived risk was negatively associated with both perceived usefulness and intention to use. In addition, perceived usefulness and perceived ease of use had the strongest positive direct impact on intention to use. Finally, perceived ease of use has positively influenced perceived usefulness.

Discussion and conclusions The purpose of this study was to propose and test a theoretical model that analyzes the antecedents of consumers’ behavioral intention to use RFID cashless payment systems in the hospitality industry. By adopting an extended version of TAM, the study empirically assessed the relationships among two exogenous variables (self-efficacy and perceived risk) and three endogenous variables (perceived usefulness, perceived ease of use and behavioral intention). Table IV and Figure 2 present the results of hypothesis testing for the research model including the path coefficients and their significance values.

The results of the study indicated that self-efficacy had a significant impact on perceived ease of use (H1 – path coefficient � 0.13). This finding was consistent with prior studies (Hasan, 2007; Jash

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