predicting rfid adoption in healthcare supply chain from the perspectives of users

Predicting RFID adoption in healthcare supply chainfrom the perspectives of users

Alain Yee-Loong Chong a,n, Martin J. Liu a,b, Jun Luo a,b, Ooi Keng-Boon c

a University of Nottingham Ningbo China, Nottingham University Business School China, Ningbo, Chinab Nottingham University Business School China, University of Nottingham, Nottingham, United Kingdomc Chancellery Division, Linton University College, Linton, Malaysia

a r t i c l e i n f o

Article history:Received 7 October 2013Accepted 26 September 2014

Keywords:RFIDInternet of thingsNeural networkHealthcareTechnology adoption

a b s t r a c t

Radio frequency identification (RFID) is an internet of things technology that provides many benefits tothe healthcare industry’s supply chain. However, a challenge faced by healthcare industry is the limitedadoption and use of RFID by physicians and nurses. This research extended existing work by integratingunified theory of acceptance and use of technology (UTAUT) (i.e. performance expectancy, effortexpectancy, facilitating conditions, social influence) and individual differences, namely personality(neuroticism, conscientiousness, openness to experience, agreeableness and extraversion) and demo-graphic characteristics (i.e. age and gender) to predict the adoption of RFID in the healthcare supplychain. Data was collected from 252 physicians and nurses. The research model was tested by employingneural network analysis. During the course of this research, 11 variables were proposed in a bid topredict the adoption of RFID by physicians and nurses. In general, individual differences are ableto predict the adoption of RFID better compared to variables derived from UTAUT. This study contributesto the growing interest in understanding the acceptance of RFID in the healthcare industry.

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1. Introduction

Managing the production and operations of organizations havechanged enormously over time due to the current competitivebusiness environment (Gunasekaran and Ngai, 2012). The opera-tions management field has now moved from offering standardizedproducts and services to one focus on offering customizations(Dobrzykowski et al., 2014). In order to achieve customizations,organizations needs to have an agile and decentralized supply chainmanagement, as well as better collaborations with customers(Chong et al., 2009; Dobrzykowski et al., 2014; Pan et al., 2013).Such increased in focusing on customers and collaborations haveled to a growing interest in services among the field of operationsmanagement (Chong and Zhou, 2014). Although operations man-agement have been well studied in the manufacturing sector, it isstill receiving considerable less attentions in service industry(Dobrzykowski et al., 2014).

One sector within the service industry where operations manage-ment play an important role is the healthcare sector. Healthcarecompanies today are constantly looking for opportunities to improve

operational efficiencies and reduce costs while continuing to improvequality of care due to the increasingly challenging value chainenvironment (Mustaffa and Potter, 2009; Wernz et al., 2014). Thehealthcare industry is faced with a complex and challenging supplychain management as it has impact on people’s health, and requiresaccurate and adequate medical supply based on the needs of patients(Mustaffa and Potter, 2009). Recent study in supply chain manage-ment has found the importance of applying information technology(IT) to improve business operations (Prajogo and Olhager, 2012).However, studies on the implementation of IT in the healthcareindustries’ supply chain management remain sparse. The implemen-tation of IT in healthcare provides many benefits such as the reductionof costs, errors, and integration of patient data. With the prevalence ofInternet and mobile technologies, healthcare providers can now alsooperate on an Internet of things environment whereby healthcareservices can be provided to anyone, anywhere and anytime (Lee andShim, 2007). One promising technology that enables the Internet ofThings environment in the healthcare industry is radio frequencyidentification (RFID). RFID can help hospitals and clinics to improvetheir management of stocks, identifying patients and keeping patientsrecords and treatments (Chong and Chan, 2012a). By using RFID,healthcare organizations can also have a fully automated solution forinformation delivery which helps reduce human errors and improveefficiencies (Lee and Shim, 2007).

Although RFID has the potential to play critical roles indelivering efficient and effective healthcare, the investment and

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n Correspondence to: Nottingham University Business School China, University ofNottingham, 199 Taikang East Road, 46300 Ningbo, China. Tel.: þ86 13957812950.

E-mail addresses: [email protected] (A. Yee-Loong Chong),[email protected] (M.J. Liu), [email protected] (J. Luo),[email protected] (O. Keng-Boon).

Please cite this article as: Yee-Loong Chong, A., et al., Predicting RFID adoption in healthcare supply chain from the perspectivesof users. International Journal of Production Economics (2014), http://dx.doi.org/10.1016/j.ijpe.2014.09.034i

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adoption in information systems such as RFID by the healthcareindustry has remained low when compared to other sectors(Chong and Chan, 2012a; Devaraj et al., 2013; Venkatesh et al.,2011a). The current low adoption of RFID in the healthcare supplychain still needs further investigations (Kumar et al., 2009). One ofthe key barriers to successful implementation of healthcare IT isthe lack of adequate training and support to the users (Venkateshet al., 2011b). Many healthcare professionals are often faced withthe dilemma of being introduced a new technology but with littletraining and process change support (Venkatesh et al., 2011b). As aresult, adoptions of technologies in the healthcare industry tend totake longer than expected when compared to other industries.Many physicians and healthcare administrators are still relying onpaper records that may not contain the latest information or mayhave higher risk of errors resulting from manual inputs. Forexample, findings show that many hospitals do not have accurateand up-to-date records of inventory such as the products’ expirydates, and stock levels. Patient records on papers are still beingrelied on physicians, and patients’ medical records which cannotbe retrieved and tracked in real time (Chong and Chan, 2012a).Instead of having real-time records of patients anytime, anywhere,much data which is generated at the point of interaction with thepatients is only being keyed into their systems at a much latertime (Venkatesh et al., 2011a). To summarize, despite the benefitsof RFID in creating a pervasive environment for the healthcareindustry, its adoption remain low.

Information system researchers in the past have examinedthe adoption of IT in the healthcare industry (Chong and Chan,2012a; Devaraj et al., 2013; Venkatesh et al., 2011b). However,many of these studies have focused at the macro level such asthe industry’s business environment and at the level of hospi-tals (Hung et al., 2010). In general, most adoption studies on thehealthcare industry are based on organizational level instead ofindividual levels. Reyes et al. (2012) for example, examined theantecedents of RFID implementation in healthcare by examin-ing 88 healthcare organizations. Although the investments onRFID usually come from an organization’s decision, firm levelbenefits can only be achieved when individuals in importantroles in the healthcare industry embrace and use the system(Venkatesh et al., 2011a). If physicians and nurses resist the useof RFID, it will be difficult for RFID to be successfully imple-mented in hospitals.

For studies that included physicians and nurses in theirsamples, they have often employed a causal–explanatory statis-tical approach in their testing of the research model. However,recent information systems researchers such as Shmueli andKoppius (2011) and Chong and Bai (2013) have called for using apredictive analytics approach in conducting information systemsresearch. Predicting analytics will enable us to develop newresearch model, as well as creating useful and practical modelwhich will help improve the acceptance of RFID among physiciansand nurses in their job activities (Chong, 2013).

Table 1 classifies the literature on healthcare technology adop-tion studies. The current study differs from prior research in manyrespects (see Table 1) and has several important contributions.First, healthcare organizations are facing various future hurdlesthat information systems researchers are well-equipped to study(Lerouge et al., 2007). One of the key hurdles is the applicationinformation technology to improve healthcare operations pro-cesses. This study examines what can influence the acceptanceof an Internet of Things technology, namely RFID, in healthcare’soperation processes, by physicians and nurses. Second, thisresearch draws its research model from different disciplines (i.e.Management Information Systems (MIS) and psychology). Pre-vious literatures on RFID usage have examined adoption decisionsbased on analytical models derived fromManagement InformationSystems (MIS) discipline such as the technology acceptance model(TAM) (Davis, 1989) and unified theory of acceptance and use oftechnology (UTAUT) (Venkatesh et al., 2003). These models havebeen applied to study an individual’s adoption on healthcaresystem (Venkatesh et al., 2011a,b), and have often focus on anindividual’s perception on the technology (e.g. perceived ease ofuse and perceived usefulness). However, an individual’s accep-tance of new IT has also been found to be influenced by theirpersonality and demographic profiles (Venkatesh et al., 2013), andthis has been neglected by previous RFID adoption studies (Chongand Chan, 2012a; Lee and Shim, 2007). Therefore, this studyaims to understand and predict healthcare worker’s adoption ofRFID based on both MIS and psychology theories by integratingboth UTAUT model with physicians and nurses’ personality anddemographic profiles. Third, this research will employ neuralnetwork to predict the adoption of RFID adoption by healthcaremodels. Neural network is found to have better predictive powercompared to previous explanatory statistical techniques (Shmueli

Table 1Relevant literature on technologies adoption in healthcare.

Study Technology Method Main model Focus on personality ofindividuals

This study RFID Predictive analytic (i.e. Neuralnetwork)

UTAUTþ Big-5 factor Yes (Big-5 factors)

Chau and Hu(2002)

Telemedicine Causal–explanatory statisticalmodelling

Technology acceptance model (TAM)þtheory ofplanned behaviour (TPB)

No

Tung et al. (2008) Electronic logisticsinformation system

Causal–explanatory statisticalmodelling

TAM No

Park and Chen(2007)

Smartphone Causal-explanatory statisticalmodelling

TAMþdiffusion of innovation No

Wu et al. (2007) Mobile healthcare system Causal–explanatory statisticalmodelling

TAM No

Kijsanayotin et al.(2009)

Health informationtechnology

Causal–explanatory statisticalmodelling

UTAUT No

Lee and Shim(2007)

RFID Causal–explanatory statisticalmodelling

Technology-push and need-pull No

Wu et al. (2011) Mobile healthcare Causal–explanatory statisticalmodelling

TAMþtheory of planned behavior (TPB) Yes (Personalinnovativeness)

Chong and Chan(2012a)

RFID Causal–explanatory statisticalmodelling

TAM No

Venkatesh et al.(2011b)

Electronic healthcare system Causal–explanatory statisticalmodelling

Social network theory No

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and Koppius, 2011), and is able to capture the non-linear RFIDadoption decisions by users. Lastly, this research extends previousIT adoption studies by focusing on the individuals use RFID, in thiscase, the physicians and nurses in the healthcare industry. Pre-vious IS/IT adoption researches have confirmed the importance ofthe role of users when it comes to adopting a new informationsystems in an organization (Venkatesh et al., 2003).The role ofusers is also important when it come to the adoption of informa-tion systems in healthcare (Jensen and Aanestad, 2007). Health-care professionals such as physicians and nurses tend to havemore autonomy in the organization, and therefore it is importantfor decision makers to understand what influence their adoptionof RFID (Jensen and Aanestad, 2007).

2. Theoretical background

2.1. Radio frequency identification (RFID) in healthcare supply chain

RFID is systems and technologies that transmit and automati-cally identify objects and people based on radio waves (Chong andChan, 2012a). RFID is one of the key technologies that build up theInternet of Things, a pervasive network environment wherebygoods are tracked throughout the supply chain and applicationsare allowed to be ran simultaneously (Kortuem et al., 2010; Ngaiet al., 2009). RFID usually consists of tags, readers and middleware.RFID operates similarly on a barcode in that it stores serialnumbers for identifying products/a product/the product andrelated information on a microchip (Ngai et al., 2008). However,unlike a barcode, RFID offers the advantage of allowing thetracking of products in the supply chain without the line of sight.RFID is also capable of storing more data compared to a barcode(Wamba and Ngai, 2012).

RFID has huge impact on various industries’ logistics, supplychains and food safety management (Ngai and Gunasekaran,2009). Despite been applied in many industries, academicresearches on RFID in the healthcare remain sparse (Ngai et al.,2009). This is surprising given that Healthcare’s operations man-agement has significant impact on its performances. Some of thechallenges faced by the healthcare industry due to poor manage-ment of operations include giving the wrong medications to thepatients, having insufficient and inaccurate pharmaceutical inven-tory control and operations, lack of patients’ identification, inabil-ity to accurately track patients’ locations, and inability to track andmanage equipment such as beds, wheelchairs, and surgical equip-ment (Chong and Chan, 2012a). RFID can help healthcare industryovercome the challenges mentioned. For example, during the avianflu outbreak, Singapore’s hospitals were able to ensure the safety ofits medical staffs by identifying and tracing possibly infectedindividuals through the use of RFID (Vanany and Shaharoun, 2008).

There are various past studies on the implementation of RFID inthe healthcare industry. Ngai et al. (2009) designed a RFID-basedhealthcare management system using an information systemdesign theory approach. Their user evaluation results shown thattheir prototype was able to improve patient safety and use ofmedication, improve pharmaceutical inventory operations andcontrol, and improve patients’ identification and in-hospital loca-tion tracking. Chong and Chan (2012a) examined the diffusiondecisions of hospitals and clinics by applying the technological–organizational–environmental (TOE) framework. Chong and Chan(2012a,b) found that the diffusion of RFID in hospitals can beinfluenced by its relative advantage, cost, security, top manage-ment support and competitive pressure. Lee and Shim (2007)developed a model based on the theory of technology-push andneed-pull to predict healthcare organizations’ intention to adoptRFID and found that variables such as presence of champions,

market uncertainty, perceived benefits and vendor pressures havea positive influence on healthcare organizations’ likelihood toadopt RFID.

Previous studies on RFID adoption have shown positive signs ofresearch and advancement in the study of RFID in the healthcareindustry (Ngai et al. 2009). However, studies on the RFID adoptiondecisions of individuals such as physicians and nurses remain sparse.Organizations’ decision to adopt RFID does not guarantee that thetechnology will be successfully deployed in the long term (Chong andChan, 2012a,b). In order to have RFID deployed successfully, it isimportant to examine stakeholders such as physicians and nurses’decisions in adopting RFID (Venkatesh et al., 2011a,b).

2.2. Unified theory of acceptance and use of technology (UTAUT)

Previous IT adoption studies on RFID have used TAM (Davis,1989), TOE (Wang et al., 2010), Diffusion of Innovation (Rogers,2010), and UTAUT (Venkatesh et al., 2003) models as their basemodels. Two of the more popular models to study individuals’adoption of IT are TAM and UTAUT (Chong et al., 2012b; Liébana-Cabanillas et al., 2014). Both TAM and UTAUT have been used tostudy the adoption of IT in the context of healthcare and healthcaresupply chain (Aggelidis and Chatzoglou, 2009; Seeman and Gibson,2009; Venkatesh et al., 2011a, 2011b), or in the context of supplychain technologies adoption. However, previous IT adoption studiesin healthcare using TAM did not manage to explain much variance(Venkatesh et al., 2011a). One possible reason for this could be thatTAM did not include other relevant factors of healthcare informa-tion systems. More recent literatures have seen an increase inapplying UTAUT to study IT adoption in healthcare or in operationsmanagement (Hennington and Janz, 2007; Ifinedo, 2012). UTAUTwas developed by Venkatesh et al. (2003) by integrating eightdifferent acceptance and use of technology models. UTAUT was ableto explain about 70% of variance in intention to adopt IT and 50% inactual use of IT. UTAUT is now one of the most widely cited andused model for the study of individual adoption of IT. TAM which isalso widely used by researchers is also contained within the UTAUTmodel, thus making UTAUT a suitable and appropriate starting pointto understand healthcare workers’ adoption of RFID (Venkateshet al., 2011a). Besides studying IT in the context of healthcare,UTAUT has been applied to study a wide range of technologies suchas course management software (Marchewka et al., 2007), Internetbanking (AbuShanab and Pearson, 2007), e-commerce (Uzoka,2008) and mobile commerce (Chong et al., 2012a).

Chang et al. (2008), for example, used variables from UTAUTsuch as facilitating conditions, perceived usefulness, and complex-ity to examine ERP system adoption from users’ perspectives.Cheng et al. (2002) also examined whether these variablesinfluence the adoption of Internet, and compare both manufactur-ing and service sectors in Hong Kong.

Fig. 1 shows an overview of UTAUT. UTAUT proposes threedirect determinants of behavioural intention to use an IT, i.e.performance expectancy, effort expectancy and social influence.There are also two direct determinants of actual use of an IT, i.e.facilitating conditions and behavioural intentions. Four contingen-cies, namely gender, age, experience and voluntariness are alsoproposed to have moderating effects of the determinants onintention to use an IT and/or actual IT use. Performance expec-tancy is defined as the degree to which the physicians/nursesthink that using RFID will help achieve significant advantages andgains in their supply chain processes compared to the previousmethods (e.g. bar code, manual recordings). Effort expectancy isdefined as the degree of ease for the physicians and nurses to useRFID. Social influence is defined as the degree to which thephysicians/nurses think that important others believe he or sheshould use RFID. Facilitating conditions is defined as the degree to

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which the physicians/nurses believe that the organizational andtechnical infrastructure exists to support his or her use of RFID.

Despite’s UTAUT being a popular IT adoption model, there hasbeen limited application of it in the healthcare industry, thus thegeneralizability of UTAUT in the healthcare industry’s supply chainprocesses needs to be further investigated (Venkatesh et al.,2011a). Examining UTAUT in a different context may result inthe creation of new knowledge. For example, Venkatesh andZhang (2010) applied UTAUT in both China and the United States,and found that the relationships in the model were different fromthe original UTAUT when taking into the consideration of culturaldifferences between Chinese and United States organizations.Unlike the original UTAUT, some modifications are being madeto this research. Facilitating conditions is proposed to be apredictor of the healthcare worker’s intention to use RFID. Thereason for this is that in the context of healthcare industry, thetraining and supports available to users may be different due tothe different nature of their job scopes. For example, physiciansmay use RFID in their interaction with patients, while an admin-istrator may use RFID to track the number of beds available.Another modification is that this study will only examine beha-vioural intention and will not include actual use behaviour. This isbecause the clinics and hospitals participating in this study havenot used RFID even though many of them are considering invest-ing in the system. We have also excluded the moderating variableswhereby age and gender will be used as predictors (see the nextsection) while voluntarism and experiences are not used. Volun-tarism is not included as the participants in this research are notbeing mandatorily asked to use RFID. Furthermore, in the contextof healthcare industry, it is difficult for organizations to mandatephysicians to use RFID as physicians operate with more autonomyin their work setting (Venkatesh et al., 2011a). Experience is alsonot included as this study is not longitudinal and all participantshave no experience of using RFID during the course of this study.Lastly, UTAUT will be expanded by incorporating variables derivedfrom psychology theory. This will be explained further in the nextsection.

2.3. Demographic profiles and big five (Big-5) personality

Previous researches on IT adoption have often expanded onTAM and UTAUT by incorporating additional constructs. Forexample, Gefen et al. (2003) in their study on online shoppingadoption added different dimensions trust to TAM. UTAUT wasalso extended by various researches such as Min et al. (2008),Ryschka et al. (2014) and Venkatesh et al. (2012) stated thatextending UTAUT by previous works have helped to expandUTAUT’s theoretical horizon. However, many of these studies have

expanded UTAUT by adding constructs without careful theoreticalconsiderations to the context being studied, and are often done onan ad hoc basis (Venkatesh et al., 2012). This paper will expandUTAUT by drawing its theory from the psychology discipline.

Studies from psychology research have widely stated that anindividual’s demographic and personality characteristics can pre-dict human behaviour. Demographic behaviours such as age andgender have been examined in past literatures, and found to beimportant predictors of IT adoption (Chong, 2013; Leong et al.,2011, 2013b). Younger people are generally more likely to adopt anew technology compared to older people (Chong, 2013). This isbecause older people may have limited computer knowledge andless training, and may have unfavourable views on computers(Igbaria and Parasuraman, 1989). Venkatesh et al. (2011a) foundthat age has moderating effects on physicians’ adoption of electro-nic medical records. However, study by Chong et al. (2012a) foundthat age is not a good predictor of IT adoption. Chong et al. (2012a)in their study on mobile commerce adoption found that there is nosignificant difference between users’ age and their use of m-commerce. They explained their findings by suggesting that sincemobile phones are such a common technology to people, thelearning curves of using mobile commerce is much less thanearlier studies which found that older people have difficulty inusing computers. Gender has also shown in previous literatures toplay a role in the use of IT (Chong, 2013; Venkatesh and Morris,2000). Venkatesh and Morris (2000) found that men are morelikely to use IT compared to women. Men are also found to befaster learner to technology when compared to women (Gefen andStraub, 1997). However, recent studies by Chong et al. (2012a) andLeong et al. (2013b) found that gender has no influence on theadoption of IT. This study will use age and gender as predictors ofRFID adoption in healthcare industry due to the different findingsin previous literatures.

An area which has received very little attentions in the ITadoption literature is individual personality (Devaraj et al., 2008).However, a basic concept underlying the user adoption of IT placesstrong emphasis on an individual’s reactions to IT, in whichpersonality can be expected to play a part (Devaraj et al., 2008).Furthermore, TAM and UTAUT are developed from the Theory ofReasoned Action (TRA), and TRA incorporated personality as avariable that affects an individual’s belief. Recent studies inpersonality psychology suggested that one way to includingindividual personality in IS models would be to apply the Big-5personality traits model (Sykes et al., 2011). The Big-5 personalitytraits model is considered by researchers to be parsimonious andcomprehensive (Devaraj et al., 2008). The traits include neuroti-cism, conscientiousness, and openness to experience, agreeable-ness and extraversion. Neuroticism refers to someone who has thetendency to be emotionally unstable and have negative experi-ence. A person who is neurotic may resist changes when theorganization wants to implement new IT. They may also havenegative feelings towards new things such as RFID, as they maynot have been exposed to the technology (Landers and Lounsbury,2006). Conscientious personality has high motivations to achieve,perform at a high level, actively plans ahead and is goal orientedwith strong sense of purpose (Devaraj et al., 2008). Such indivi-duals also spend more time in work related activities, and aremore likely to try out new technologies introduced in theirworkplace that are useful and may improve their productivity(Venkatesh et al., 2013). Individuals who are open to experienceare willing to take risks and try new and different things. Theyhave high level of curiosity, and are intrinsically motivated toexplore and try new IT (Venkatesh et al., 2013). Agreeablenessrefers to an individual who is kind, considerate, helpful andcooperative (Devaraj et al., 2008). An individual with high agree-able personality is likelier to be cooperative and accommodating

Fig. 1. Original UTAUT.Source: Venkatesh et al. (2003).






when asked to consider using a new technology such as RFID(Devaraj et al., 2008). Extraversion refers to an individual who issociable, active and willing to engage with others (Venkatesh et al.,2013). Extroverts are motivated to gain social status, and this is animportant motivation for him or her to adopt a new technology(Rogers, 2010; Venkatesh et al., 2000). Therefore, an individualwith high extraversion is more likely to adopt RFID so that theycan obtain high opinions from significant others (Devaraj et al.,2008).

Based on the above discussions, this research will integrateUTAUT with demographic profiles and personality of users topredict healthcare workers’ intention to adopt RFID in theirworkplace. The research model is shown is Fig. 2.

2.4. Neural network analysis in RFID implementations

Previous researches on IT adoption and acceptance haveemployed advanced statistical modelling methods such as struc-tural equation modelling to support their empirical studies(Shmueli, 2010; Shmueli and Koppius, 2011). Such trend is alsobecoming popular in the study of operations management (Shahand Goldstein, 2006). Previous studies on the adoption of RFID orIT adoption in healthcare have mainly employed the causal–explanatory statistical modelling, whereby the statistical inferenceis applied to test hypotheses and to evaluate explanatory modelssuch as UTAUT and TAM (Shmueli, 2010; Shmueli and Koppius,2011). Studies such as those conducted by Chong (2013) areexamples of “preferences regression” whereby they all share thesame prior assumptions that the processes of an individual’sadoption decisions are linear compensatory (Chiang et al., 2006;Chong, 2012). These models assume that a shortfall in an adoptionfactor such as performance expectancy can be compensated byimproving other factor such as price value (Chiang et al., 2006;Chong, 2012). However, studies have shown that not all IT adoptionprocesses of evaluation are compensatory (Chiang et al., 2006;

Chong, 2012; Chong and Bai, 2013). One alternative approach thatcan address the limitation in traditional linear–compensatoryapproach is neural network.

Neural network is a “massively parallel distributed processormade up of simple processing units, which have a naturalpropensity for storing experimental knowledge and making itavailable for use” (Haykin, 1994, p. 2). Similar to a human brain,a neural network is able to acquire knowledge through a learningprocess (Chiang et al., 2006; Chong, 2012). The knowledge isstored by the interneuron connection strengths (synaptic weights)(Haykin, 1994). The neural network model will use learningalgorithm to analyse the data set, and the synaptic weights ofthe neural weight will be modified to attain the desired designobjective (Chong et al., 2013).

Besides the ability capture non-linear, non-compensatoryassumptions, neural network is also employed here as it is ableto better predict RFID adoptions in the healthcare industry.As Shmueli and Koppius (2011) suggested, there is now a needto integrate predictive analytics into information systems research.Advantages of predictive model such as neural network includebeing able to create useful and practical model, and help in theorybuilding and theory testing (Shmueli and Koppius, 2011). Neuralnetwork’s adaptivity also allows it to respond to structural changesin the data generation process, and it can be easily re-trained todeal with changes in the environment (Chiang et al., 2006; Chonget al., 2013; Garson, 1998). Recent studies in IT adoptions that hasapplied neural network to predict IT adoption include those byChong and Chan (2012b) and Leong et al. (2013a). Therefore, thisresearch will employ neural network to capture and predict thenon-compensatory healthcare workers’ RFID adoption decisionprocess.

3. Methodology

3.1. Sampling and data collection

This study was conducted in a private medical group that isexploring at the possibility of implementing RFID solutions in theiroperations such as inventory control and tracking, and patients’identification and recording. The group has recently successfullyimplemented several IT initiatives such as a web based accounting,invoicing system and electronic medical records. The group hasmore than 100 clinics in various locations throughout Malaysia.The head-quarter has a centralize control of medical, financial andmedical records. The clinics’ supply chain management is animportant part of the medical group given that the headquarterneeds to do group purchasing of inventories such as medicalsupplies and medicines, and distribute them to the relevant clinics.The head-quarter is also in charge of producing invoices from theindividual clinic and charge relevant companies when theiremployees visit one of the clinics. The clinics also run on 24 hoperations. Therefore, there is a need to have automated, pervasivecomputing system, and the medical group’s director is consideringimplementing RFID. The survey was distributed to 500 physiciansand nurses in the medical group, and they completed and finallyreturned usable surveys that amounted to 252. Out of the 252respondents, 107 are physicians, and 145 are nurses. 82 of therespondents are males and 170 are females. The average age of therespondents is 38.

3.2. Variables and measures

The 11 constructs in this research were measured by 28 items.Age and gender are both single item measurement but theother items are measured based on a 5 point Likert Scale. UTAUT

Personality

Demographics

UTAUT

Performance Expectancy

Effort Expectancy

Social Influence

Facilitating Conditions

Age

Gender

Neuroticism

Conscientiousness

Openness

Agreeableness

Extraversion

RFID adoption

Fig. 2. Research model.






construct (i.e. Performance expectancy, effort expectancy, socialinfluence and facilitating conditions) is adopted from Venkateshet al. (2003), while the Big-5 personality constructs (i.e. neuroti-cism, conscientiousness, openness to experience, agreeablenessand extraversion) from Gosling et al. (2003). The output variablesof this research (i.e. Behavioral Intention to adopt RFID byhealthcare workers) are adapted from Venkatesh et al. (2003).

3.3. Reliability and validity measures

The reliability constructs are evaluated by on Cronbach’s alpha.As shown in Table 2, the Cronbach’s alpha values of all constructsis greater than 0.70, thus confirming construct reliability (Hairet al., 1998). Convergent validity was examined by three criteria:all item loadings are significant; composite reliability more than0.70; and the average variance extraction (AVE) was greater than0.50 (Hair et al., 2013). As shown in Table 2, the constructs aredeemed as reliable and valid as they satisfied the criteria men-tioned earlier.

3.4. Neural network analysis

The back-propagation neural network is applied to analyse thedata using Tiberius v7.0.7 and SPSS 17.0 This is one of the mostcommon neural network approach and has being applied to studyIT adoption such as mobile commerce (Chong, 2012).

A typical back-propagation neural network process is shown inFig. 3. The back-propagation process involves the weighted inputsbeing added and processed by an activation function, followed bybeing outputted to the next layers of neurons (Chong et al., 2013).Back-propagation neural network will usually have three layerswhich are the input layer, middle layer which consists of thehidden notes, and the output layer (Garson, 1998).

A three layer neural network is shown in Fig. 3. Values between0 and 1 will be assigned to the initial weights and biases. Theneural network will then be provided with sets of inputs andoutput trainings. In this study, the inputs will involve variablesfrom UTAUT, demographic profiles and personality of the health-care workers. The output will be the behavioural intention toadopt RFID by healthcare workers. Fig. 4 shows the neural networkmodel developed for this research.

The neural network algorithms will then model the process bywhich the input is mapped onto the output (Chong et al., 2013).The training process is iterated to reduce the estimation errorsbetween the actual output of the network and the desired output.

The difference between the actual output and the desiredoutput will be calculated and back-propagated to the previouslayers (Chong et al., 2013). The Delta rule will be applied to adjustthe connection weight in order to reduce the output errors. Thisprocess is back-propagated to the previous layer until it reachesthe input layer (Chiang et al., 2006).

Fig. 4. Neural network model for RFID adoption by healthcare workers.

Table 2Validity and reliability testing.

Crossloadings

AVE Compositereliability

Cronbachsalpha

RFID adoptions 0.624–0.888n 0.672 0.889 0.829Effort expectancy 0.781–0.810n 0.625 0.870 0.801Facilitating conditions 0.575–0.787n 0.527 0.815 0.696Performanceexpectancy

0.803–0.937n 0.768 0.930 0.898

Social influence 0.627–0.849n 0.558 0.832 0.742Neuroticism 0.845–0.951n 0.860 0.925 0.839Conscientiousness 0.882–0.944n 0.834 0.910 0.807Openness 0.918–0.919n 0.805 0.892 0.759Agreeableness 0.914–0.940n 0.809 0.894 0.779Extraversion 0.887–0.908n 0.844 0.915 0.815

n po0.05.

Wi3

Wi1

Wi2

Connection Weights

Yi

Summation function

Activation function

Neuron i

Output of node i

X1

X2

X3

Xn

Win

Fig. 3. Artificial neuron.Source: Adapted from Chong et al., (2013).






3.5. Validations of neural networks

Multilayer perceptron training algorithm was applied to trainthe neural network. In order to avoid over-fitting of the neuralnetwork model, cross validations were conducted. One challengein neural network is that there is no heuristic way for determiningthe hidden nodes (Sexton et al., 2002). As a result, we used theapproach by Sexton et al. (2002) and Wang and Elhag (2007) byexamining 1–10 hidden nodes. Networks with six hidden nodeswere found to be complex enough to map the datasets withoutincurring additional errors to the neural network model. Ourneural network therefore consists of 11 predictors, six hiddennodes, and one output variable.

Root mean squared error (RMSE) was used to determine theaccuracy of the mode. A 10-fold cross validation was performedwhereby we used 90% of the data (i.e. 227) to train the neuralnetwork, while the remaining 10% (i.e. 25) was used to measurethe prediction accuracy of the trained network. Table 3 shows theRMSE of the validations. From Table 3, the average RMSE for thetraining model is 0.470 while the testing model is 0.466. We cantherefore be confident that the network model is reliable incapturing the numeric relations between the predictors and out-puts. This research also benchmarked our result with multipleregression analysis. The RMSE value from regression analysisperformed was 0.512. Therefore, our neural network performedbetter than regression model.

Fig. 5 shows the predicted-by-observed chart which displays ascatterplot of the predicted values of RFID adoption on the y axis, andthe observed values on the x axis for the combined training and testingsamples. From the figure, the neural network model in this researchdoes a reasonable job in predicting RFID adoption as the values areroughly plotted along the 45-degree line starting at the origin.

3.6. Sensitivity analysis

Sensitivity analysis performance was computed by averagingthe importance of the input variables in predicting the output forthe 10 networks (Chong et al., 2013). The importance of thepredictor variable is a measure of how much the network’smodel-predicted value changes for different values of the pre-dictor variable (Chong et al., 2013). We calculated the normalizedimportance by dividing the importance values by the largestimportance value, expressed as a percentage (Chong et al., 2013).

Table 4 shows that all 11 predictors are found to be relevant toall 10 networks. The result showed that the most importantpredictors of RFID by healthcare workers are their personality anddemographic profiles. Variables derived from UTAUT are also found

to be predictors of RFID adoption. However, these variables havelower importance values compared to the personality and demo-graphic variables. Effort expectancy and performance expectancyare the two most important predictors among the UTAUT variables.The overall results suggest that it is important to extend existing ITadoption models by including users’ personality and demographics.

We also further analysed our data by examining the correlationbetween the predictors and output variable (Sexton et al., 2002).Our result showed that the relationships between neuroticism, ageand gender (0¼male, 1¼female) have negative relationships withhealthcare workers’ intention to adopt RFID; while other predic-tors have positive relationships with healthcare workers’ intentionto adopt RFID.

4. Discussions

The healthcare industry is one of the fastest growing industriesand having an Internet of Thing environment would improveefficiencies of hospitals and clinics, while at the same timereducing possible human errors (e.g. data entry error). Therefore,it is important to understand the RFID acceptance decisions ofhealthcare. This research extended prior research on RFID adop-tion by examining on individual differences of healthcare workers,namely physicians and nurses. We also extended prior research onRFID adoption in the healthcare industry (Chong and Chan, 2012a).Our results showed that personalities such as extraversion, neu-roticism, conscientiousness, agreeableness, and openness toexperience are among the most important predictors of RFID

Fig. 5. Predicted-by-observed chart.

Table 4Normalized variable importance.

Predictors Normalized importance (%)

Openness to experience 100Extraversion 63Conscientiousness 35Age 32Agreeableness 30Gender 23Performance expectancy 21Effort expectancy 21Social influence 16Facilitating conditions 11Neuroticism 6

Table 3Full validation results of neural network model.

Network Training Testing

1 0.487 0.4922 0.462 0.4813 0.475 0.4414 0.442 0.4545 0.463 0.4326 0.474 0.4767 0.484 0.4818 0.452 0.4939 0.491 0.45110 0.469 0.456

Mean 0.470 0.466Standard deviation 0.016 0.022

Note: Regression analysis was conducted as a benchmark and the RMSE value was0.512.






adoption. This is consistent with findings from Sykes et al. (2011)and Venkatesh et al. (2013) which found that personalities of usersplay an important role in their acceptance of IT. In particularly, ourresult showed that in the order of importance, openness toexperience, conscientiousness, extraversion, agreeableness arestrong personality predictors of RFID adoption.

However, both gender and age are stronger predictors thanneuroticism. This finding is consistent with Sykes et al. (2011) andVenkatesh et al. (2013) as neuroticism was found to have nosignificant relationship with IT adoption. The demographic profilesshowed that younger physicians and nurses are more likely toadopt RFID when compared to older physicians and nurses. Thissupports the findings from Venkatesh et al. (2011a). Our resultshows that future generation of physicians and nurses are likelierto adopt RFID, which is an encouraging finding. However, thechallenge for the healthcare industry is that physicians and nurseswho are older also possess more experience and knowledge, andinterrupting their workflow may affect the quality of care theydelivered (Venkatesh et al., 2011a). One result also showed thatmale healthcare workers are also more willing to adopt RFID. Thisis consistent with past findings from Venkatesh and Morris (2000).

This research also found that although UTAUT variables canpredict RFID adoption by healthcare workers, their predictionstrength is lower than personality and demographic variables.This is consistent with Venkatesh et al. (2011a) who stated thatMIS theory such as UTAUT need to be modified when studying indifferent context, and in this case, a different country just as thisresearch was conducted in Malaysia. The strongest predictors fromUTAUT are performance expectancy, social influence, facilitatingconditions and effort expectancy.

5. Conclusion and implications

Recent IS scholars have proposed that future IT adoptionstudies to move away from traditional technology adoptionmodels. This research has responded to this need by integratingUTAUT with personality and demographic profiles of users. Thefindings of this research have several theoretical and practicalimplications.

5.1. Theoretical implications

First, this research developed a model to predict the adoptionof RFID by healthcare workers. By integrating variables frompsychology, health informatics and MIS theory, we can betterunderstand their relative importance for RFID adoption. Ourfindings showed that the strongest drive to adopt RFID is due toindividual differences and personalities. Previous studies on tech-nology adoptions have often neglected individual differences andpersonalities. The predictive power of other MIS adoption modelscan therefore be improved by incorporating personality variables.The Big-5 factors applied in this research is a useful framework forstudying relevant domains of personality as this research showsthat they are better predictors than UTAUT variables in the contextof our study.

The second theoretical contribution of this research is healthinformatics in supply chain management. Although Internet ofThings technologies such as RFID has been applied in manyindustries, their application in the healthcare sector has remainedslow. By developing this research model developed from MIS andpsychology theories, it will further complement previous studieson RFID adoption. Our findings also showed that it is important toconsider factors from individual differences and UTAUT in under-standing healthcare workers’ adoption of RFID. These factors havedifferent influence on the use of RFID and therefore different

implications for the management of RFID implementation. Forexample, different personalities among physicians and nursescould possibly influence their propensity to use RFID. Theseindividual factors must be considered before rolling out theimplementation of RFID in hospitals and clinics. These factorsshould also be paid attention to when rolling out across a group ofphysicians and nurses who are asked to use RFID (Venkatesh et al.,2011a). Effort expectancy of RFID is the strongest predictor of RDIFadoption among the UTAUT variables. Therefore, hospitals andclinics are clearly informed and explain clearly to physicians andnurses how RFID can help improve their work performance. Effortexpectancy is the least important predictor, suggesting thatphysicians and nurses will not be affected by the potentialdifficulties in learning how to use the system. This is supportedby the fact that facilitating conditions is also an importantpredictor of RFID adoption. Therefore, hospitals and clinics mustmake sure that they have sufficient supports, trainings andtechnical helps available to physicians and nurses. The importanceof social influence shows that physicians and nurses are likelier toadopt RFID if people who are important to them think they shoulduse the system. Therefore, in terms of workplace, nurses andphysicians are more likely to adopt RFID if their supervisors orsenior management encourage them to use the system. Seniormanagement should also let physicians and nurses know that byimplementing RFID, it will help improve the performances inhospitals and clinics performance.

The last theoretical implication of this research is the applica-tion of predictive analytic approach (i.e. neural network) to studyRFID adoption. Previous MIS scholars have called for the use ofpredictive analytic approach due to the lack of application inempirical information systems research. This research thereforeexamines an IT model that has been extensively examined byexplanatory statistical modelling. Our result based on neuralnetwork shows that integrating individual differences to MISadoption model can offer good prediction of users’ adoption ofIT, in this research, healthcare workers’ adoption of RFID.

5.2. Practical implications

There are several practical implications of this study. Health-care industry introducing IT such as RFID may consider havingdifferent training programmes to users instead of a one size fit allprogramme. For example, our findings showed that males aremore likely to adopt RFID than females; healthcare industry canaddress this by having more IT training for female healthcareworkers to help improve their acceptance of RFID. Rather thaneducate healthcare workers on RFID and ways to use the technol-ogy alone, healthcare industry should have more awarenesscampaigns on the usefulness of RFID to the healthcare workersas this will influence their adoption decisions. People who havenegative beliefs toward RFID due to their personality may beselected for training programmes designed to overcome theirnatural inclination (Devaraj et al., 2008). Therefore, healthcareworkers select people with different personality and providedifferent trainings to them as extra efforts may be needed toconvince people with certain personality types to accept RFID.

Another consideration is that since younger workers are morelikely to accept RFID, it is possible to developed a buddy systemas recommended by Venkatesh et al. (2011a) to pair a junior–senior physician or junior–senior nurse team to work together inlearning and using the RFID system. This also complements thefindings whereby social influence will predict RFID acceptance,and perhaps present the right atmosphere for paired colleaguesto both adopt and in the long-run promote the use of RFID in theworkplace.






Healthcare companies can also provide incentives to indivi-duals who have natural inclination to reject the use of RFID. Whenpersonality of the healthcare workers inhibit them from usingRFID extensively, it is possible that suitable reward options willencourage them to use the system, and they might eventuallyexperience the usefulness of the system. Lastly, healthcare indus-try planning to implement RFID should note that the changemanagement policies will not be able to fit everyone. It isimportant for senior management of the healthcare companiesto build an inclusive framework that takes account of individualdifferences when forming change management strategies.

6. Future studies

There are several limitations of this research. First, this researchwas conducted in Malaysia based on clinics belonging to one largehealth group. Therefore, future research should conduct theirstudies on different companies/countries to examine the wide-spread and generalizability of our research model. Second, ourresearch has applied neural network for prediction purpose butwas not able to examine the causal relationships between theinput and output variables. Future studies can apply both neuralnetwork approach with other techniques such as regressionanalysis or structural equation modelling to test the causalrelationships of the proposed model.

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