J Med Syst (2015) 39:172 DOI 10.1007/s10916-014-0172-4
EDUCATION & TRAINING
Determinants of RFID Adoption in Malaysia’s Healthcare Industry: Occupational Level as a Moderator Suhaiza Zailani & Mohammad Iranmanesh & Davoud Nikbin & Jameson Khoo Cheong Beng
Received: 6 August 2014 / Accepted: 25 November 2014 # Springer Science+Business Media New York 2014
Abstract With today’s highly competitive market in the healthcare industry, Radio Frequency Identification (RFID) is a technology that can be applied by hospitals to improve operational efficiency and to gain a competitive advantage over their competitors. The purpose of this study is to investigate the factors that may effect RFID adoption in Malaysia’s healthcare industry. In addition, the moderating role of occupational level was tested. Data was collected from 223 managers as well as healthcare and supporting staffs. This data was analyzed using the partial least squares technique. The results show that perceived ease of use and usefulness, government policy, top management support, and security and privacy concerns have an effect on the intent to adopt RFID in hospitals. There is a wide gap between managers and healthcare staff in terms of the factors that influence RFID adoption. The results of this study will help decision makers as well as managers in the healthcare industry to better understand the determinants of RFID adoption. Additionally, it will assist in the process of RFID adoption, and therefore, spread the usage of RFID technology in more hospitals.
Keywords RFID . Healthcare . Adoption . Malaysia
“This article is part of the Topical Collection on Education & Training”
S. Zailani : M. Iranmanesh (*) University of Malaya, Kuala Lumpur, Malaysia e-mail:
[email protected] D. Nikbin University of Malaya, Melaka, Malaysia J. K. C. Beng Universiti Sains Malaysia, Penang, Malaysia
Introduction Many people annually die due to medication related errors in Malaysia. There are many reasons that could lead to such errors, such as similar medication names, labels, and packaging, as well as staff shortages, fatigue and carelessness [1–3]. To address this issue, hospitals have begun to use various technologies to guard the medication activities. Radio frequency identification (RFID) technology a technology that has various applications [4, 5] which assist in the daily work of hospitals such as reducing medication errors and increaseing patient safety. Through RFID, healthcare businesses can improve their organizational performance and competitiveness [6–8]. Besides operations improvements, RFID can also help improve patients’ safety [9, 10]. Despite the great promise of RFID in hospitals, not all hospitals adopt RFID without hesitation. Hence, the issue of “what factors influencing the adoption of RFID in a healthcare setting” becomes an important question for all healthcare administrators. The healthcare industry is one of the fastest growing service industries in Malaysia [11]. The Malaysian government has actively promoted Malaysia as a destination for medical or health tourism, whereby its high quality medical service and affordable costs are able to attract many customers from different countries. In addition to the competition between Malaysian hospitals and clinics for business, the Malaysian healthcare industry is also competing with countries such as Singapore and Thailand [11]. Besides offering good medical services, one way in which the Malaysian healthcare industry can compete is to be more efficient and effective in their operations [12]. Although RFID is able to help Malaysia to achieve this, many hospitals are still reluctant to implement RFID [9]. Several hospitals in Malaysia, Singapore, and Indonesia conducted pilot studies on the use of RFID [9]. However, upon completion of the pilot studies, only the Singapore healthcare industry adopted RFID, while their Malaysian and Indonesian counterparts did not follow suit. Past literature has attempted to investigate the factors that affect the adoption of
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RFID in retail [13], logistics [14], and manufacturing [15], but there is a lack of empirical research on the determinants of RFID adoption in the healthcare industry. The studies in this industry focus more on the barriers of RFID adoption [9], applications of RFID in the healthcare industry [16, 17] and risk associated with RFID in hospitals [18, 19]. Therefore, to date, factors that drive RFID adoption in hospitals are still not clearly or fully identified. To fill up this gap, the determinants of RFID adoption in hospitals are investigated in the present study. Furthermore, although the adoption of RFID within a hospital needs to involve decision makers (top and mid managers) and RFID users in hospitals (healthcare and supporting staff), little research, if any, has been done to elucidate the moderating effect of the occupational level. Finally, most of the previous RFID studies were limited to management level’s willingness to adopt RFID [20, 21]. Our study therefore investigated the potential moderating effect of occupational level. The findings of this study are useful because they can improve the understanding of the potential drivers of RFID adoption by managers and healthcare staff in the Malaysian healthcare industry. With this knowledge pool, decision makers and policy makers will be able to make strategic future plans, and focus on critical success factors and address pitfalls, to successfully implement adoption of RFID technology in the hospitals.
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Hawrylak et al. [18] categorized the application of RFID in the healthcare industry into three areas, namely tracking and managing inventory, locating assets, patients, and staff members, and improving the quality of patient care. The inventory management ability of RFID allows hospitals to reduce overhead inventory and use all supplies before they expire [24]. Locating assets, patients, and staff ability enable hospitals to monitor staff and patient location which makes it possible to better contain diseases and implement effective quarantine and isolation areas [25]. RFID can also improve the quality of patient care by assisting the patients in following the prescribed treatment schedule [26]. The use of RFID technology in the healthcare industry is rather new compared to its use in other industries such as libraries, retail, manufacturing, logistics and supply chain [9]. The adoption of RFID in the healthcare industry, is generally seen as the “next disruptive innovation in healthcare” [27]. Various researchers have assereted that RFID technology can have a number of benefits in the healthcare system, among them Wang et al. [25] and Tzeng et al. [28] specified that RFID technology has a great capability to considerably reduce cost, improve patient safety and medical service as well as improve the business process. Although RFID technology has lots of benefits to the healthcare industry, RFID adoption in healthcare has not been as striking as anticipated [22]. Therefore, in the present study the factors that may effect the adoption of RFID technology in hospitals were investigated.
RFID technology in healthcare Radio Frequency Identification (RFID) is a fast developing technology that uses radio waves for data collection and transfer. It can capture data efficiently and automatically without human intervention [22]. Traditionally, bar codes were used in healthcare to identify patients and ensure that the right drugs were delivered to the right individuals. There are a number of issues with the barcode system. First, the barcode requires a lineof-sight between the barcode scanner (reader) and wristband. This can cause difficulty if the patient needs to be identified while they are sleeping if the barcode is not visible. In this case, the staff member must wake the patient to identify them. Second, the barcode scanner uses optical sensors to read the barcode and if the wristband becomes dirty the barcode cannot be read or worse read incorrectly. If the barcode is read incorrectly, the patient may be identified as another patient. This is problematic because a major use of the barcode is to match a patient’s medication to the patient. RFID offers solutions to both of these issues. RFID is a wireless technology and does not require a lineof-sight to be read [18]. Although RFID costs more than bar codes, it has many advantages over the older technology which makes it as a good alternative [23]. First, bar codes are printed, so they will fade after a period of time. Second, because a line-ofsight between the RFID tag and RFID reader is not required there is no problem if the RFID tag is dirty. Third, an RFID reader can read hundreds of RFID tags at once.
Model conceptualization and hypothesis development Extensive literature reviews on the adoption of RFID technology in an organization such as a hospital shows that several determinants can positively or negatively influence the adoption process [e.g. 20, 22]. Taking these published suggested determinants into consideration, coupled with local scenarios, this study will focus on six variables as the determinants to RFID adoption intention in the local healthcare industry, namely perceived ease of use, perceived usefulness, perceived relative advantage, government policy, top management support, and security and privacy concerns (Fig. 1). Occupational level is considered as a moderating variable to explore the potential difference in drivers of RFID adoption intention among decision makers and RFID technology users in hospitals. In the following sections, the relationship will be established and the hypothesis will be developed: Perceived ease of use Ease of use is the degree to which a person believes that using a technology will be free of effort [29]. The complexity of a technological innovation is an important variable before a decision is made to adopt the innovation. When one believes that technology will be free of effort, it will increase the behavioral intention of individuals to adopt the technology
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physician perceived that the use of technology would enhance their job performance and productivity, it would have a favorable influence on their intention to adopt technology. Hence, the study hypothesizes that: H2: Perceived usefulness is positively associated with the hospitals’ adoption intention towards RFID technology
Perceived Ease of Use (PEU) H7 H1a
Perceived Usefuleness (PU) H3a
Adoption Intention (AI)
Perceived Relative Advantage (PRA) Government Policy (GP) Top Management Support (TMS) Security and Privacy Concerns (SPC)
Fig. 1 Proposed theoretical model
[30]. Due to the complex working environment and busy schedule, the physician will intend to adopt if they perceive that the technology is easy to control, understand, flexible and convenient to learn and operate. Although, the effect of perceived ease of use (PEU) on technology adoption intention has been empirically shown in many studies [e.g. 31–34] but sometimes ease of use has been shown to have both direct and indirect effects on adoption intention [31, 32], whereas in other cases only it has an indirect effect through perceived usefulness [33, 34]. The direct effect suggests that perceived ease of use could improve attitude toward adoption regardless of the technology’s usefulness. By contrast, the indirect effect stems from the situation where, the easier a technology is to use, the more useful it is perceived to be, thus, the more positive one’s attitude and intention toward using the technology [35]. Therefore, the following hypotheses are developed: H1a: Perceived ease of use is positively associated with the hospitals’ perceived usefulness towards RFID technology. H1b: Perceived ease of use is positively associated with the hospitals’ adoption intention towards RFID technology.
Perceived usefulness Perceived usefulness (PU) refers to individuals’ perceptions about a specific technology that will help them to perform their jobs better [29]. A significant body of research has shown that PU is a strong determinant of users’ intention to adopt technology [33, 34, 36]. In fact, perceived usefulness has been found to be the most significant factor in acceptance of technology in the workplace, even more important than ease of use [33, 36]. From past studies, PU is also found to be one of the most significant determinants of physician’s behavioral intention to adopt technology [30, 37, 38]. If the
Perceived relative advantage Perceived relative advantage (PRA) is the degree to which an innovation is perceived as being better than the previous idea [39]. Innovative technologies that are perceived by individuals as having a greater relative advantage will be adopted more rapidly than other technologies [40]. Past research shows that PRA is the most important predictor of the rate of adoption of innovative technologies [39, 41, 42]. Adopting RFID technology has numerous benefits (e.g. tags can be read without a line of sight, multiple tags can be read simultaneously, tags can cope with harsh environmental conditions, information can easily be updated, and automatically tracked) for hospitals when compared to bar codes, the previous technology [23]. Comparing perceived usefulness with relative advantage, the former reflects the belief that a technology helps perform a function while the latter is focused on the degree to which an innovation is perceived to be better than its precursor. Although the two concepts are related, they are distinct and may play complementary roles in shaping adoption attitudes [34]. Despite their conceptual distinctions, direct empirical examination of their relative roles has not been investigated in the healthcare industry. Kulviwat et al. [34] is the only study that shows, perceived relative advantage has an indirect effect on adoption intention through perceived usefulness. With regard to the flow of effects from relative advantage to adoption intention, both direct and indirect effects are expected. Relative advantage is posited to influence perceived usefulness, and thereby adoption intention, in much the same way as explained earlier regarding perceived ease of use; that is, the healthcare staff are likely to judge RFID to be useful to the extent that it is believed to have advantages over the alternative(s). This is the indirect effect. However, not all advantages are necessarily considered useful by healthcare staff. Often, hospitals tout the advantages RFID has over the previous technology, that may not be considered “useful” from the healthcare staff’s perspective. Yet, these advantages may still influence the adoption intention toward RFID technology. This is the direct effect. Therefore, the following hypotheses are developed: H3a: Perceived relative advantage is positively associated with the hospitals’ perceived usefulness towards RFID technology. H3b: Perceived relative advantage is positively associated with the hospitals’ intention to adopt RFID technology.
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Government policy Government policy includes government’s financial support, training curriculum, specification and policy stability [43]. Government regulation can have both positive and negative effects on adoption of Technology [44, 45]. Without governmental action, it would be even more risky to make the decision to adopt new technologies and processes [46]. When adopting RFID, organizations expect to receive support from the government with respect to policies, incentives and subsidies to accelerate the rate of adoption [46, 47]. Similar to other developing countries, government support is essential for adoption of RFID technology in Malaysian hospitals, because the hospitals especially the public ones, rely highly on government support such as allocating budget, recommending reliable and professional vendors and designing RFID policy. The study of Chang et al. [43] and Chang et al. [48] found that government policies have a positive impact on hospitals trying to adopt new technology. Therefore, the following hypothesis is developed: H4: Government Policy is positively associated with the hospitals’ adoption intention towards RFID technology. Top management support Top management support is critical for creating a supportive climate and for providing adequate resources for the adoption of new technologies [15, 49]. As the complexity and sophistication of technologies increases, top management can provide a vision and commitment to create a positive environment for adoption [50]. In any professional organization, there are two primary sources of influence from managerial sites; the first is at the enterprise level, which refers to top management and the second refers to the management of the department unit to which the individuals belong. In most cases, top management indicate the importance of technology to the organization through their funding and resource allocation, and, thereafter, the department management impact on individual behavior by strengthening and clarifying the importance of signals emerging from organization management [51]. In fact, the day-to-day awareness and perception of organizational personnel are to some extent influenced by the messages and signals emanating from their department management as those imposed by top management. According to prior research one of the key factors in adopting a new technology in a health care organization is that of top management support [38, 52, 53]. In the present study, top management support refers to whether or not the executives understand the nature and functions of RFID technology and therefore fully support the development of it. Top management plays an important role because RFID implementation involves changes in the workflow and business processes of the health care firms. Therefore top management
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support is crucial to ensure the resistance to the changes can be overcome [54]. The study of Chong and Chan [18] found that top manager’s support will affect RFID adoption in hospitals. Therefore, the following hypothesis is developed: H5: Top management support is positively associated with the hospitals’ adoption intention towards RFID technology. Security and privacy concerns In this age of cyber-crime, security is a major concern and barrier to any technology. Security is defined as the protection of transaction and customer details from both internal and external fraud or criminal usage. If the security doubt of a certain innovation is not addressed or solved, it will severely impact the adoption intention towards the innovation [40]. Data security is an important consideration in the health care domain. When an RFID tag is associated with a patient, it can contain a unique identification number that can associate with any type of personal information, such as patient name, gender, home address, medical history. This information is highly mobile and sensitive [22]. The level of security of RFID systems can be counted as a disadvantage of the system [55]. There are many security threats regarding RFID adoption such as eavesdropping on the communication between the tag and the reader during data capture, skimming, interference, hacking, cloning, and fraud [56]. Therefore; lack of security of systems can be a serious barrier for adoption of the technology [9, 57]. Furthermore, these privacy issues raised by the technology are amongst the factors that slow down the adoption of RFID [9, 58]. The feeling of being watched by hospital administrators during break time and patient care have caused many nurse’s unions to prevent the adoption of new technology [59]. RFID systems cause a major ethical concern regarding privacy violations because of its surveillance potential. This might act as barrier to adoption of technology [57]. Surveillance capability of RFID can put pressure on healthcare staff [59]. Hence, the study hypothesizes that: H6: Security and privacy concerns are negatively associated with the hospitals’ intention to adopt RFID technology. Occupational level The willingness to support the changes, and adoption by the end-users is critical to ensuring the success and growth of RFID technology (or any technology) in the hospitals. A topdown management approach might be the norm when a technology is being introduced, but the continual success and growth will depend on the uptake and the willingness of the end-users to adopt the technology. The hospitals’ managers and staff concerns regarding the adoption of a new technology is different in some cases. For example, healthcare and supporting staff’s willingness to adopt a technology could be dependant upon whether that new technology can lower
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their medical duties [60] while being easy to use [61], whereas hospitals’ managers might be looking for a technology that can reduce costs [62, 25] and has a return on investment [63]. Although the adoption of RFID within a hospital needs to involve decision makers (top and mid managers) in addition to RFID technology users (healthcare and supporting staff), the potential drivers of adopting RFID technology is expected to differ between decision makers and users in hospitals. Little research, if any, has been done to reveal the moderating effect of the occupational level of users on RFID adoption. Most RFID studies to date have been limited to only management level stakeholders [20, 21]. Therefore, the following hypothesis is developed: H7: Occupational level of users moderates the effect of (a) perceived ease of use, (b) perceived usefulness, (c) perceived relative advangtage, (d) government policy, (e) top management support, and (f) security and privacy concerns on the hospitals’ intention to adopt RFID technology.
Research methodology Measure of constructs The present study employed a quantitative survey with a structured questionnaire. The questionnaire is divided into 3 sections with a total of 28 items: Respondents’ basic information, determinants of adoption intention (perceived ease of use, perceived usefulness, perceived relative advantage, government policy, top management support, and security and privacy concerns), and adoption intention. Besides the respondents’ basic information, the other items were measured using five-point Likert scales anchored by ‘strongly disagree’ and ‘strongly agree’. Content validity considers how representative and comprehensive the items are in creating the experimental constructs. To establish content validity, literature review to scope the domain of the construct is used [64]. The items tapping the theoretical constructs were developed based on an extensive literature review and adapted from Venkatesh [65], Kifle et al. [66], Ramayah and Lo [67], Riquelme and Rios [40], Shaqrah [68], Rosenbaum [19], thus satisfies content validity. The items in the survey questionnaire are shown in Table 1.
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clinical specialists/physicians, nursing matrons and nursing sisters. Healthcare staff will include medical officers, house officers, clinical pharmacists, registered nurses and radiologists. Supporting staff are community nurses, attendants, pharmacy assistants, procurement officers and laboratory staff. All these individuals are key stakeholders in the adoption and implementation of any technology in the healthcare industry, both as decision makers as well as end users. Decision makers play an important role in the beginning stage when RFID technology is being introduced, and end-users willingness to adopt RFID technology is important for successful implementation of this technology in hospitals. As of such, their input is highly sought to ensure the successful rollout of any plans. A lack of prior experience in the healthcare industry was supplemented by the list of key hospitals obtained via the Malaysia Ministry of Health (MOH) and Malaysia Medical Council (MMC) to determine which major hospitals to include in this study. To ensure the reliability of respondents, only hospitals that had participated in RFID related activities such as conferences or workshops, or had been contacted by the RFID industrial alliance as potential users were chosen. In addition, a filtering question at the beginning of the survey questionnaire ensured that the respondents had adequate knowledge on RFID. The survey was conducted using questionnaires which were sent out via hospital administrators or human resource departments using the drop and pickup method. Out of 300 questionnaires sent out to the hospitals, only 238 were collected, yielding a return rate of 79.33 %. Among them, 15 questionnaires were only partially completed, and, hence, not usable; therefore, the usable response rate was about 74.33 %. Analysis The Partial Least Squares (PLS) technique was applied to analyse the casual relationships between constructs using the software application SmartPLS 3.0. The PLS approach was selected due to the exploratory nature of the research [69]. The two-step approach was utilized in data analysis as suggested by Henseler et al. [70]. The first step involves the analysis of the measurement model, while the second step tests the structural relationships among the latent constructs. The two-step approach aims at establishing the reliability and validity of the measures before assessing the structural relationship of the model.
Data collection and the sample The unit of analysis for this study are decision makers (top and mid managers) and RFID end users (healthcare and supporting staff) working in various hospitals in Malaysia. Top managers includes department heads, hospital directors, assistant directors, heads of committees and consultants. The mid managers consist of committee members, managers,
Results Profile of respondents The final sample consisted of 40 (17.9 %) top managers, 67 (30.0 %) mid managers, 63 (28.3 %) healthcare staff, and 53
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Measurement model evaluation
Constructs
Items
Factor loadings
CR
Perceived ease of use (PEU)
RFID devices and applications are clear and easily understood. RFID operations are easy to use. RFID applications are easily learnt and used. RFID technology is user friendly. RFID in Health Management Information System (HMIS) will improve the healthcare services offered to patients. RFID in HMIS will increase my efficiency and productivity as a healthcare and support staff. RFID in HMIS will reduce healthcare and support staffs’ work related paperwork RFID in HMIS will improve the overall operations of the organization RFID in HMIS increases the overall efficiency and productivity of the organization. RFID in HMIS offers additional advantages in comparison to manual information management. RFID in HMIS is more convenient than manual information management. RFID in HMIS increases efficiency and productivity of healthcare & support staffs. RFID in Health Management Information System (HMIS) implementation depends on a Government’s vision & policies. RFID in HMIS is influenced by the transfer of various technologies, and facilitated by a Government’s policies. RFID in HMIS is facilitated by a Government’s ICT policies to create awareness and promotes uptake of the technology. RFID in HMIS’s success is dependent on top management’s buy-in, support and assistance towards its implementation. RFID in HMIS implementation and operation needs financial, relevant resources and commitment from to top management. RFID in HMIS will reduce the security of data. RFID in HMIS will reduce the privacy of healthcare staff.
0.696 0.734 0.730 0.748 0.658
0.818 0.529
0.836 0.506
0.751 0.703 0.756 0.685 0.728
0.824 0.609
0.807 0.805 0.703
0.798 0.570
RFID tags with different data can be created and replace authentic tags. My willingness to use RFID in HMIS is very high if it is implemented. I intent to use RFID in HMIS when it is implemented.
0.680 0.853 0.830
Perceived usefulness (PU)
Perceived relative advantage (PRA)
Government policy (GP)
Top management support (TMS)
Security and privacy concerns (SPC) Adoption intention (AI)
AVE
0.843 0.711 0.782
0.780 0.640
0.817 0.720 0.795
0.776 0.537
0.829 0.709
CR Composite reliability, AVE Average variance extracted
(23.8 %) supporting staff. The male respondents contribute 44.8 % and female respondents 55.2 %. There were 68 (30.5 %) respondents between 26 and 35 years old, followed by, 65 (29.1 %) respondents between 36 and 45 years old, 55 (24.7 %) respondents between 46 and 55 years old, 30 (13.5 %) respondents above 55 years old, and 5 (2.2 %) respondents below 25 years old. In terms of the educational status of respondents, 139 (62.3 %) respondents have an initial degree and 84 (37.7 %) respondents have a post-graduate degree. The job tenure of 48.9 % of the respondents is less than 10 years and 51.1 % of them are above 10 years.
Measurement model analysis The reliability and validity of the reflective constructs needs to be assessed. Composite reliability (CR) needs to be assessed in connection with internal reliability which is similar to Cronbach’s Alpha. The CR of all constructs were above 0.7 (Table 1), satisfying the Hair et al. [71] rule of thumb. Hair et al. [72] suggested accepting items with loadings of at least
0.6. Since the loadings associated with each of the scales were all greater than 0.6, individual item reliability was judged acceptable. The convergent validity was evaluated using the average variance extracted (AVE). The AVE of all constructs was above 0.5, signifying satisfactory degree of convergent validity [73]. We used two approaches to test the discriminant validity of the constructs. First, we examined the cross-loading of the indicators, which revealed that no indicator loads higher on an opposing construct [74]. Second, we applied the criterion of Fornell and Larcker [73] and tested whether each construct’s AVE is greater than its squared correlation with the remaining constructs (Table 2). Both analyses confirm the discriminant validity of all constructs.
Assessment of the structural model With the satisfactory results in the measurement model, the structural model was evaluated subsequently. The predictive accuracy of the model was evaluated in terms of the portion of
J Med Syst (2015) 39:172 Table 2
Perceived Ease of Use (PEU)
Discriminant validity coefficients PEU
PEU PU PRA GP TMS PS AI
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0.727 0.507 0.464 0.219 0.249 −0.187 0.355
PU
PRA
GP
TMS
PS
AI
H1a= 0.256***
Perceived Usefuleness (PU)
0.712 0.659 0.469 0.355 −0.392 0.441
0.781 0.451 0.351 −0.397 0.312
Adoption Intention (AI)
H3a= 0.540***
0.755 0.392 −0.450 0.399
Perceived Relative Advantage (PRA)
0.800 −0.371 0.365
0.733 −0.419
Government Policy (GP)
0.842
variance explained. The results suggest that the model is capable of explaining 33.2 % of the variance in adoption intention and 48.6 % in perceived usefulness. Besides estimating the magnitude of R2, researchers have recently included predictive relevance developed by Stone [75] and Geisser [76], as additional model fit assessment. This technique represents the model adequacy to predict the manifest indicators of each latent construct. Stone-Geisser Q2 (cross-validated redundancy) was computed to examine the predictive relevance using a blindfolding procedure in PLS. Following the guidelines suggested by Chin [77], a Q2 value of greater than zero implies the model has predictive relevance. In the present study, a value of 0.212 was obtained as an average crossvalidated redundancy (for both endogenous variables) which is far greater than zero. In sum, the model exhibits acceptable fit and high predictive relevance. Nonparametric bootstrapping was applied [78] with 2000 replications to test structural model. The structural model resulting from the PLS analysis is summarized in Fig. 2. As shown, all of the hypotheses were supported except for H3b. The results also show that, perceived ease of use has no indirect effect (β=0.056, p>0.05) on intention to adopt RFID, whereas perceived relative advantage has an indirect effect (β=0.117, p
