understanding the determinants of rfid adoption in the manufacturing industry

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Understanding the determinants of RFID adoption in the manufacturing industry Yu-Min Wang a, , Yi-Shun Wang b , Yong-Fu Yang b a Information Management at National Chi Nan University, Nantou Hsien, Taiwan b Information Management at National Changhua University of Education, Changhua, Taiwan article info abstract Article history: Received 23 September 2009 Received in revised form 28 February 2010 Accepted 14 March 2010 Radio frequency identication (RFID) is one of the most promising technological innovations, with the potential to increase supply chain visibility and improve process efciency. It allows remote identication of an object using a radio link. However, it has yet to see high rates of adoption in the manufacturing industry. Thus, effort is required to identify determinants affecting RFID adoption in the manufacturing industry. Based on the technologyorganizationenvironment (TOE) framework of Tornatzky and Fleischer (L.G. Tornatzky, M. Fleischer, The processes of technological innovation, Lexington Books, 1990), nine variables (relative advantage, compatibility, complexity, top management support, rm size, technology competence, information intensity, competitive pressure, and trading partner pressure) are proposed to help predict RFID adoption. Data collected from 133 manufacturers in Taiwan is tested against the proposed research model using logistic regression. The results and implications included in our study contribute to an expanded understanding of the determinants that affect RFID adoption in the manufacturing industry. © 2010 Elsevier Inc. All rights reserved. Keywords: Radio frequency identication Technologyorganizationenvironment framework Technology adoption Innovation adoption 1. Introduction Radio frequency identication (RFID) is a generic term for technologies that use radio waves to automatically identify individual physical objects. Once goods are attached with RFID tags, their whereabouts can be tracked automatically by radio readers, providing greater inventory visibility, improved business and control processes, and enhanced supply management efciency [1,2]. Therefore, many businesses are in various stages of applying the advantages of RFID to experimental projects to improve operational efciency and to gain competitive advantage [3]. The RFID technology market is thus a rapidly growing market, with a total value that is expected to top US$7 billion by 2008 and increase to US$26.88 billion by 2017 [4]. While RFID has been discussed in the literature as a technology that can provide several advantages, both strategic and operational, to its adopters, the RFID adoption rate is not growing as fast as expected [2,5]. This suggests more effort is necessary to understand the process of adoption of the technology and to identify factors affecting the RFID adoption decision [6]. The technologyorganizationenvironment (TOE) framework is a reasonably theoretical basis for analyzing technology adoption at the rm level [7]. Using the TOE framework, this study developed and validated an adoption model for RFID technology in the manufacturing industry. The rest of this paper is organized as follows. Section 2 introduces the background of RFID and the literature review of TOE framework and prior RFID adoption studies. In Section 3, we present the research model and hypotheses. This is followed by the description of the research methods used in data collection and measure purication. Section 5 presents an analysis of the data. The last two sections offer a discussion and conclusions, respectively. Technological Forecasting & Social Change 77 (2010) 803815 Corresponding author. 470, University Rd., Puli, Nantou, Taiwan. E-mail address: [email protected] (Y.-M. Wang). 0040-1625/$ see front matter © 2010 Elsevier Inc. All rights reserved. doi:10.1016/j.techfore.2010.03.006 Contents lists available at ScienceDirect Technological Forecasting & Social Change

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Page 1: Understanding the determinants of RFID adoption in the manufacturing industry

Technological Forecasting & Social Change 77 (2010) 803–815

Contents lists available at ScienceDirect

Technological Forecasting & Social Change

Understanding the determinants of RFID adoption in themanufacturing industry

Yu-Min Wang a,⁎, Yi-Shun Wang b, Yong-Fu Yang b

a Information Management at National Chi Nan University, Nantou Hsien, Taiwanb Information Management at National Changhua University of Education, Changhua, Taiwan

a r t i c l e i n f o

⁎ Corresponding author. 470, University Rd., Puli, NE-mail address: [email protected] (Y.-M. Wan

0040-1625/$ – see front matter © 2010 Elsevier Inc.doi:10.1016/j.techfore.2010.03.006

a b s t r a c t

Article history:Received 23 September 2009Received in revised form 28 February 2010Accepted 14 March 2010

Radio frequency identification (RFID) is one of the most promising technological innovations,with the potential to increase supply chain visibility and improve process efficiency. It allowsremote identification of an object using a radio link. However, it has yet to see high rates ofadoption in the manufacturing industry. Thus, effort is required to identify determinantsaffecting RFID adoption in the manufacturing industry. Based on the technology–organization–environment (TOE) framework of Tornatzky and Fleischer (L.G. Tornatzky, M. Fleischer, Theprocesses of technological innovation, Lexington Books, 1990), nine variables (relativeadvantage, compatibility, complexity, top management support, firm size, technologycompetence, information intensity, competitive pressure, and trading partner pressure) areproposed to help predict RFID adoption. Data collected from 133 manufacturers in Taiwan istested against the proposed research model using logistic regression. The results andimplications included in our study contribute to an expanded understanding of thedeterminants that affect RFID adoption in the manufacturing industry.

© 2010 Elsevier Inc. All rights reserved.

Keywords:Radio frequency identificationTechnology–organization–environmentframeworkTechnology adoptionInnovation adoption

1. Introduction

Radio frequency identification (RFID) is a generic term for technologies that use radio waves to automatically identifyindividual physical objects. Once goods are attached with RFID tags, their whereabouts can be tracked automatically by radioreaders, providing greater inventory visibility, improved business and control processes, and enhanced supply managementefficiency [1,2]. Therefore, many businesses are in various stages of applying the advantages of RFID to experimental projects toimprove operational efficiency and to gain competitive advantage [3]. The RFID technology market is thus a rapidly growingmarket, with a total value that is expected to top US$7 billion by 2008 and increase to US$26.88 billion by 2017 [4].

While RFID has been discussed in the literature as a technology that can provide several advantages, both strategic andoperational, to its adopters, the RFID adoption rate is not growing as fast as expected [2,5]. This suggests more effort is necessary tounderstand the process of adoption of the technology and to identify factors affecting the RFID adoption decision [6]. Thetechnology–organization–environment (TOE) framework is a reasonably theoretical basis for analyzing technology adoption atthe firm level [7]. Using the TOE framework, this study developed and validated an adoption model for RFID technology in themanufacturing industry.

The rest of this paper is organized as follows. Section 2 introduces the background of RFID and the literature review of TOEframework and prior RFID adoption studies. In Section 3, we present the research model and hypotheses. This is followed by thedescription of the research methods used in data collection and measure purification. Section 5 presents an analysis of the data.The last two sections offer a discussion and conclusions, respectively.

antou, Taiwan.g).

All rights reserved.

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2. Background

2.1. RFID

RFID stands for the radio frequency identification, a generic term for technologies and systems that use radio waves to transmitand automatically identify people or objects. A generally possible context for an RFID system is shown in Fig. 1. The RFID systemtypically consists of three basic components: tags (transponders), readers, and middleware [8,9]. It is always connected to anenterprise application system for data processing in support of business activities.

A tag usually comprises a microchip and an antenna that are attached to or embedded in an object [10,11]. The microchipcontains identification information andmay have other application data (e.g. price, cost, location, andmanufacture date, etc.) [12].The antenna's functions are to send and receive data, and power up the tag by absorbing radio-frequency energy [10,11,13]. TheRFID tags come in various forms and functional characteristics. The cost and functions of one tag depend on its form, operatingfrequency, data capacity, range, power supply, presence or absence of a microchip, and read/write memory [8,14]. The RFID tagsare usually classified according to their power supply, usually in the form of a battery. “Passive tags” do not have their own powersupply, and therefore all energy required for the tag operations must be drawn from the reader's radio signals [14]. “Active tags”typically have internal read andwrite capabilities, their own power supply, and can transmit their signals over a long distance [10].“Semi-passive tags” fall somewhere between the two types of tags mentioned above. Semi-passive tags have their own powersupply for the microchip's standby operation but not for broadcasting a signal to the reader; they draw energy from the readerduring active communication [8,11].

The reader, also called an interrogator, is a device that consists of a radio-frequency module, a control unit, and one or manyantennas to read/write the information stored in the RFID tags by transmitting and receiving radio-frequency waves [9,15,16].Basically, the reader instructs antennas to generate the proper radio-frequency field. The area is called the interrogation zonewithin which a reader can read the tag. When a passive tag moves into the zone, it draws power from the reader's radio-frequencyand sends out the programmed response [8,13]. Active tag does not reflect the signal from the reader [10]. Because an active taghas its own power supply and transmitter, it does not have to wait for the reader's signal and can send its data at certain intervalsas defined by the system [17].

In order tomake application systems independent of various types of readers and their different connection interfaces, and freefrom filtering, processing and cleaning huge amount of data, there is a need of an intermediate layer between the RFID readers andthe enterprise application systems [8,18]. The requirement is fulfilled by the middleware. The middleware functions may include:(1) reader and device management: provide a common interface to configure, monitor, deploy, and issue commands directly toreaders; (2) data management: filter raw data and pass on only useful information to the appropriate applications; (3) applicationintegration: provide integrated RFID data and connect disparate applications within the enterprise; and (4) partner integration:provide collaborative solutions like business-to business integration between trading partners [10,13,18].

RFID is based on radio wave propagation. Radio waves have the ability to penetrate matter, enabling the system to read a tag ina good that is not visible. This permits the user to identify or track such goods without scanning a barcode [1]. By providing precisedata on product location, product characteristics, and product inventory levels, RFID promises to eliminate manual inventorycounting, warehouse mispicking, and order numbering mistakes. Manufacturers can benefit from RFID in such areas as inventoryvisibility, labor efficiency, and improved fulfillment, and supply chain management efficiency [2,9]. These advantages will bringopportunities for improvement and transformation in various processes of the supply chain.

2.2. The technology–organization–environment framework

Tornatzky and Fleischer [7] proposed the technology–organization–environment (TOE) framework to study the adoption oftechnological innovations. They argue that the decision to adopt a technological innovation is based on factors in theorganizational and environmental contexts, as well as characteristics of the technology itself. This framework thus envisions athreefold context for adoption and implementation of technological innovations: technological context, organizational context,and environmental context.

Fig. 1. RFID system context.

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The technological context refers to the technologies relevant to the firm. This includes the existing technologies, as well as theemerging technologies relevant to the firm. Many characteristics of the technology can influence its adoption. The organizationalcontext describes the characteristics of an organization. Common organizational characteristics include firm size, degree ofcentralization, formalization, complexity of its managerial structure, the quality of its human resources, and the amount of slackresources available internally [7,20]. Organization characteristics can also constrain or facilitate the adoption and implementationof technological innovations. The environmental context is the arena in which an organization conducts its business. This includesthe industry and dealings with business partners, competitors, and government [7]. They are factors external to an organizationthat can present constraints and opportunities for technological innovations.

The Rogers theory of innovation diffusion [21] is one of the most widely applied theories in the prediction of organizational-level technology adoption. Rogers identified five technological characteristics as antecedents to any adoption decision: relativeadvantage, compatibility, complexity, trialability, and observability. In addition, he also emphasized three groups of adoptionpredictors: leader characteristics, internal characteristics of the organization, and external characteristics of the organization. Theleader characteristic can be viewed as a specific internal organizational property [22]. The external characteristics of theorganization refer to the environmental context in the TOE framework. Thus, Rogers’ theory of innovation diffusion is consistentwith the TOE framework [23,24].

Furthermore, findings from innovation-adoption research are consistent with the TOE framework [25–27]. For example,Iacovou et al. found that threemajor determinants influenced EDI adoption in the small business context [26]. These determinantsare organizational readiness, external pressure, and perceived benefits. The organizational readiness belongs to the organizationalcontext in the TOE framework, while the external pressure is a factor in the environmental context in the TOE framework. The ideaof perceived benefits refers to the level of recognition of the relative advantage that an EDI technology can provide to theorganization. Therefore, the concept of perceived benefits is part of the technical context in the TOE framework.

The TOE framework has consistent empirical supports and has been found useful in understanding the adoption oftechnological innovations [20,22–24,28–32]. For example, Kuan and Chau applied the TOE framework to study EDI adoption insmall businesses [30]. Hong and Zhu examined six variables based on the TOE framework to successfully differentiate non-adopters from adopters of e-commerce [29]. Zhu et al. studied how TOE factors influenced e-business assimilation at the firm level[23,24]. Table 1 summarizes the relevant studies based on the TOE framework. The TOE framework can be used to studyorganizational adoption of general IT innovation [20,22–24,28,29,31,32], as well as specific IT innovation, such as EDI [30].

The weaknesses of the TOE frameworkmay be twofold: (1) it may not explicitly point out that what are themajor constructs inthe framework and the variables in each context, and (2) specific determinants identified within the three contexts may varyacross different studies. In spite of the weaknesses, the TOE framework provides a good starting point when analyzing andconsidering suitable factors for understanding the innovation-adoption decision, because it has many consistent empiricalsupports.

Table 1Previous studies using the TOE framework in investigation of the adoption of technological innovations.

Study Innovation studied Determinants

Chau and Tam [20] Open system ● Technology: perceived benefits; perceived barriers; perceived importance ofcompliance to standards, interoperability, and interconnectivity

● Organization: complexity of IT infrastructure; satisfaction with existing systems;formalization on system development and management

● Environment: market uncertaintyZhu et al. [22] E-business ● Technology: technology competence

● Organization: firm scope; firm size● Environment: consumer readiness; competitive pressure; lack of trading partner readiness

Zhu et al. [23] E-business ● Technology: technology readiness; technology integration● Organization: firm size; global scope; managerial obstacles● Environment: competitive intensity; regulatory environment

Zhu et al. [24] E-business ● Technology: relative advantage; compatibility, costs and security concern● Organization: technology competency; organizational size● Environment: competitive intensity; partner readiness

Gibbs and Kraemer [28] E-commerce ● Technology: technology resources● Organization: perceived benefits; lack of organizational compatibility; financial resources; firm size● Environment: external pressure; government promotion; legislation barriers

Hong and Zhu [29] E-commerce ● Technology: technology integration; web functionalities; EDI use● Organization: web spending; perceived obstacles● Environment: partner usage

Kuan and Chau [30] EDI ● Technology: perceived direct benefits; perceived indirect benefits● Organization: perceived financial cost; perceived technical competence● Environment: perceived industry pressure; perceived government pressure

Zhang et al. [31] IT usage ● Technology: IT infrastructure● Organization: IT management● Environment: e-government; government regulation and promotion

Xu et al. [32] Internet ● Technology: technology competence● Organization: firm size; global scope; enterprise integration● Environment: competition intensity; regulatory environment

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Drawing upon the empirical evidence, combined with literature review and theoretical perspectives discussed earlier, weargue that the TOE framework is an appropriate foundation for studying RFID adoption. RFID is enabled by the technologicaldevelopments in radio and automated identification, driven by organizational factors such as firm size and management support,and influenced by environmental factors related to business partners and competitors.

2.3. Prior research on RFID adoption

Brown and Russell conducted an exploratory investigation to identify the factors that may influence RFID adoption in SouthAfrican retail organizations [33]. A combination of quantitative and qualitative data based on six retailers was collected andanalyzed. The findings of Brown and Russell supported the applicability of the TOE framework in the RFID adoption research. Theyalso found that the RFID adoption intention was explained by technological factors (i.e., relative advantage, compatibility,complexity, and cost), organizational factors (i.e., top management attitude, information technology expertise, organization size,organizational readiness), and external factors (i.e., competitive pressure, external support, and existence of change agents).

Schmitt et al. reviewed related works and induced 25 adoption factors in the technology, organizational, and environmentcategories [34]. They extracted the five most important factors affecting RFID adoption and diffusion in the automotive industry atthat moment. These factors were compatibility, costs, complexity, performance, and top management support. Most of thesefactors belong to the class of technology characteristics. Schmitt et al. suggested that the RFID adoption and diffusion was still in anearly stage and therefore basic technological issues had to be solved first. Thus, the organizational and environmental factors wereless important than technological factors at that moment. Moreover, inter-organizational factors were not so important, becausemost the RFID deployments in the automotive industry were of the intra-organizational applications at that time.

FossoWamba et al. identified 21 factors in the four categories that were related to the evaluation and decision to invest in RFID[35]. The four categories are similar to the TOE framework. The technology and automation categories are the technology contextin the TOE framework. The resource category is the organizational context and the supply chain category belongs to theenvironmental context in the TOE framework. In addition, they revealed the differences in the relative importance of the 21 factorsfor RFID investment decisions between RFID adopters and non-adopters. The firms that have not yet adopted RFID are moreconcerned about “acquisition costs”, “replacement costs” and “ongoing costs”. Firms adopting RFID are more concerned about“information visibility” and “competitive differentiation” and less concerned about the “costs”. Both RFID adopters and non-adopters are driven by the promise of greater data accuracy, improved information visibility, service quality, process innovation,and track and trace capabilities.

Leimeister et al. conducted a cross-national comparison of perceived strategic importance of RFID for CIOs in Germany and Italy[36]. They found that perceived potentials of RFID influenced perceived strategic importance of RFID and that the composition ofperceived potentials was diversified across different cultures and industries. However, they also found that perceived strategicimportance of RFID influenced CIOs’ intention to invest in RFID regardless of the cultural contexts. Moreover, company size wasfound to have no effect on perceived strategic importance of RFID and the RFID experience was still low in the two countries.

In summary, we conclude the above-mentioned RFID adoption research as twofold:

(1) Despite there are diverse factors affecting RFID adoption among prior studies’ findings, all these factors can be classifiedeither as technological, organizational, or environmental contexts. Therefore, it is feasible to utilize the TOE framework toexplore the RFID adoption issue.

(2) Most of the prior studies show the importance of the technological factors in affecting RFID adoption. However, the effectsof organizational and environmental factors on RFID adoption vary across different cultural and industrial contexts. Thus,there is a need to analyze the determinants of RFID adoption in different cultural and industrial contexts in order to obtain abetter understanding of RFID adoption.

3. Research model and hypotheses

A wide range of factors has been found in the literature. Instead of repeating them, we chose to focus on a few factors that arebelieved to be important in understanding and explaining RFID adoption. The researchmodel was proposed as shown in Fig. 2. It isdesirable to use adoption as the dependent variable in our study since it adequately captures whether firms have implementedRFID technology. We regarded adoption as a binary variable; that is, firms either have implemented or have not implemented theRFID technology under study.

The model consists of nine determinants that are hypothesized to have a direct effect on firm adoption of RFID technology. Asthis study focuses on identifying factors that can predict the category of a firm (i.e. adopter or non-adopter of RFID technology), therelationships among the nine factors were not in our research scope.

3.1. Technology context

Rogers identified five technological characteristics as antecedents to any adoption decision: relative advantage, compatibility,complexity, trialability, and observability [21]. Many studies, including the meta-analysis of 75 diffusion articles conducted byTornatzky and Klein [37], found that only relative advantage, compatibility, and complexity are consistently related to innovation

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Fig. 2. The research model for RFID adoption.

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adoption. Grover [38] and Lee and Kim [39] directly excluded the trialability and observability constructs in their researchmodels.Therefore, three technological characteristics –relative advantage, complexity, and compatibility – are included in the model.

3.1.1. Relative advantageRelative advantage is defined as the degree to which an innovation is perceived as providing greater organizational benefits

than the idea it supersedes or the status quo [21]. It is reasonable that organizations take into consideration the advantages thatstem from adopting innovations. Once all goods receive RFID tags, their whereabouts can be tracked automatically by radioreaders, which give complete inventory visibility and supply chain management efficiency [2]. Therefore, RFID is expected to beable to give organizations greater competitive advantage [3,6,12]. In sum, companies which perceive higher relative advantages inRFID technology tend to be more likely to adopt the technology. Accordingly, the following hypothesis is proposed:

H1. Relative advantage will have a positive effect on RFID adoption.

3.1.2. ComplexityComplexity is the extent to which an innovation is perceived as relatively difficult to understand and use [40]. Since complexity

of an innovation can function as an inhibitor for successful implementation, it is usually negatively associated with adoption[37,41,42]. People may not have confidence in the RFID system because it is relatively new to them [1]. It may take users a longtime to understand and implement the technology. The proliferation of standards and protocols and the diversity of thetechnology make RFID implementation a very complex task [43,44]. Therefore, the following hypothesis is proposed:

H2. Complexity will have a negative effect on RFID adoption.

3.1.3. CompatibilityCompatibility is the degree to which an innovation is perceived as being consistent with the needs or the existing practices of

the potential adopters [21,37]. High compatibility has been identified as a facilitator for innovation adoption [25]. Rather thansimply substituting for existing data collection technologies such as bar codes within the current processes, implementing RFID isoften combined with process innovations that create orders of magnitude improvements in supply chain business models [45].Resistance to change may be an important issue on the implementation of RFID systems [1]. Therefore, compatibility may be animportant determinant of RFID adoption. The following hypothesis is proposed:

H3. Compatibility will have a positive effect on RFID adoption.

3.2. Organization context

3.2.1. Top management supportTop management support is an important factor in the adoption of new technologies and has been found to be positively

related to adoption [38,42]. Top management can provide a vision, support, and a commitment to create a positive environmentfor innovation [39]. Top management support is more critical for RFID technologies since the RFID implementation requires

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adequate resources, process reengineering, and user coordination. Top management also can send signals to various parts of theorganizations about the importance of the innovation [46]. Accordingly, the following hypothesis was proposed:

H4. Top management support will have a positive effect on RFID adoption.

3.2.2. Firm sizeMany studies have found that firm size facilitates innovation [7,38,42,47,48]. Larger firms usually have more resources to

experimentwith new innovations and have greater ability to absorb the risks and costs of implementing innovations [27,49]. Becausethe cost of RFID tags and the systems needed to read and track tags are still important issues, only large companies have the financialresources to invest in prototype RFID installations at this time [44]. Accordingly, the following hypothesis was proposed:

H5. Firm size will have a positive effect on RFID adoption.

3.2.3. Technology competenceTechnology competence, also called technological readiness, consists of information technology (IT) infrastructure and IT

professionals [23,24]. IT infrastructure refers to installed network technologies and enterprise systems, which provide a platformon which the RFID applications can be built. IT professionals refer to possessing the knowledge and skills to implement RFID-related IT applications. In general, the development of an RFID system is still relatively new to many organizations [1].Implementing RFID applications requires new IT skills, new IT components and adaptation of existing information systems [12,43].Therefore, we can expect that firms with greater technology competence are in a better position to adopt RFID. Theseconsiderations lead to the following hypothesis:

H6. Technology competence will have a positive effect on RFID adoption.

3.3. Environment context

3.3.1. Competitive pressureCompetitive pressure has been identified as an important determinant of IT adoption [22,30]. As market competition increases,

firmsmay feel theneed to seek competitive advantage through innovations. ByadoptingRFID,firmsmaybenefit frombetter inventoryvisibility, greater operation efficiency, and more accurate data collection [12,43]. Thus, the following hypothesis was proposed:

H7. Competitive pressure will have a positive effect on RFID adoption.

3.3.2. Trading partner pressureSeveral empirical studies have found that pressure from trading partners may be a facilitator for innovation adoption

[26,28,50]. Not surprisingly, requests from powerful partners (ones that generate a large proportion of sales or a large proportionof the firm's profits) are a critical factor in adoption of specific innovation. When a firm's dominant customers or suppliers haveadopted an innovation, the firm may adopt the innovation to show its fitness as a business partner [51]. Several major buyers andretailers have come to recognize the potential usefulness of RFID technology as a way of tracking physical goods across the supplychain. This has led some of them to mandate its adoption by their trading partners [10]. For example, Wal-Mart has mandated itssuppliers adopt RFID as a condition of doing business with it [44]. Thus, the following hypothesis was proposed:

H8. Trading partner pressure will have a positive effect on RFID adoption.

3.3.3. Information intensityInformation intensity refers to the degree to which information is present in the product or service [27]. Information-intensive

products are generally more complicated to order or use, and they require more accompanying information. Such products canbenefit from the strategic use of IT [52]. Firms in more information-intensive environments are more likely to adopt new IT thanthose in less information-intensive environments [53]. Therefore, the information intensity of products in the businessenvironment may have a bearing on the adoption of an innovation [38,54,55]. Compared with the bar codes, RFID tags can storemore information, accept updates and be read faster [19,56]. Therefore, the following hypothesis was proposed:

H9. Information intensity will have a positive effect on RFID adoption.

4. Research methodology

4.1. Construct measures

The principal constructmeasures were based on existing instruments. Itemsweremodified to fit the RFID context. Items for thefirm size, complexity, and information intensity were adapted from Grover [38]. Four items pertaining to the top managementsupport construct were taken from Soliman and Janz [57]. The measures for technology competence, competitive pressure, andpartner pressurewere adapted from Iacovou et al. [26] and Lin [58]. Items for the complexity, compatibility, and relative advantageconstructs were adapted from Grover [38] and Ranmmurthy et al. [59]. A five-point Likert scale ranging from “(1) stronglydisagree” to “(5) strongly agree”was used for all items. Table 2 summarizes the measurement items of the independent variables.

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Table 2Measurement items of the independent variables.

Variables Measurement items

Top management support TS1. My top management is likely to invest funds in RFID.TS2. My top management is willing to take risks involved in the adoption of the RFID.TS3. My top management is likely to be interested in adopting the RFID applications in order to gain competitive advantage.TS4. My top management is likely to consider the adoption of the RFID applications as strategically important.

Firm size FS1. The capital of my company is high compared to the industry.FS2. The revenue of my company is high compared to the industry.FS3. The number of employees at my company is high compared to the industry.

Technology competence TC1. The technology infrastructure of my company is available for supporting RFID-related applications.TC2. My company is dedicated to ensuring that employees are familiar with RFID-related technology.TC3. My company contains a high level of RFID-related knowledge.

Information intensity II1. The product/service in my industry generally requires a lot of information to sell.II2. The product/service in my industry is complicated or complex to understand or use.II3. The ordering of products in my industry by customers is generally a complex process.

Competitive pressure CP1. My company experienced competitive pressure to implement RFID.CP2. My company would have experienced a competitive disadvantage if RFID had not been adopted.

Trading partner pressure PP1. The major trading partners of my company encouraged implementation of RFID.PP2. The major trading partners of my company recommended implementation of RFID.PP3. The major trading partners of my company requested implementation of RFID.

Complexity CX1. My company believes that RFID is complex to use.CX2. My company believes that RFID development is a complex process.

Compatibility CM1. The changes introduced by RFID are consistent with my firm's existing beliefs/values.CM2. RFID is compatible with existing information infrastructure.CM3. The changes introduced by RFID are consistent with existing practices.CM4. RFID development is compatible with my firm's existing experiences with similar systems.

Relative advantage RA1. My company expects RFID to help lower inventory costs.RA2. My company expects RFID to help quick data capture and analysis.RA3. My company expects RFID to help reduce paperwork.

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The dichotomous dependent variable, adoption, measured whether a company was an adopter or non-adopter of RFID (0: non-adopter, 1: adopter). The construct was operationalized via a yes/no response to the question “Has my company adopted an RFIDapplication?”

4.2. Data collection and sample profile

Data for our study were collected using a questionnaire survey administered in Taiwan. The questionnaire consists of threeparts: one for business-related items, another for assessing the nine predictors, and the third for asking whether the company was

Table 3Sample profile.

Category Number Percentage

Employee number● b1000 75 56.4%● 1000–2000 19 14.3%● N2000 39 29.3%

Capital (NT$ million)● b1000 41 30.8%● 1000–5000 64 48.1%● N5000 28 21.1%

Annual sales (NT$ million)● b10,000 76 57.1%● 10,000–30,000 33 24.8%● N30,000 24 18.0%

Company age (years)● ≦10 35 26.3%● 11–20 36 27.1%● 21–40 45 33.8%● ≧41 17 12.8%

RFID adoption● Yes 55 41.4%● No 78 58.6%

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Table 4Factor analysis results and α coefficients.

TS FS TC II CP PP CX CM RA

TS1 .75TS2 .75TS3 .70TS4 .83FS1 .91FS2 .87FS3 .86TC1 .88TC2 .87TC3 .73II1 .51II2 .81II3 .77CP1 .83CP2 .78PP2 .82PP3 .66CX1 .91CX2 .90CM1 .60CM2 .84CM3 .89CM4 .89RA1 .83RA2 .85RA3 .54α coefficient .83 .90 .89 .72 .76 .71 .91 .88 .78

TS: top management support; FS: firm size; TC: technology competence; II: information intensity; CP: competitive pressure; PP: trading partner pressure; CX:complexity; CM: compatibility; RA: relative advantage.All factor loadings with absolute values smaller than 0.5 were suppressed.

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an adopter or non-adopter of RFID. 500 firms from the top-1000 firms in the manufacturing industry were randomly selected. Thestudy questionnaires were mailed to the IS executives of the 500 firms. We received 133 useful responses. The response rate wasthus 26.6%. Sample profiles are shown in Table 3. Among the valid responses, 55 (41.4%) had adopted RFID and 78 (58.6%) had not.

4.3. Instrument validation

Factor analysis was conducted to assess the construct validity of the measures. The 133 responses were examined using aprincipal-components factor analysis as the extraction technique and varimax as the orthogonal rotation method. In order toassess the fit between the items and their constructs, all of the primary factor loadings should be greater than 0.5 and have nocross-loadings [60]. Only one item (PP1. The major trading partners of my company encouraged implementation of RFID) waseliminated because of cross-loadings. Factor analysis was run once again to determine whether the factor structure remainedstable. Table 4 demonstrates a good match between each factor and related items.

Cronbach's α coefficient is used to measure the reliability. As shown in Table 4, all coefficients for the constructs in this studyare higher than the threshold value of 0.7.

5. Data analysis and findings

The composite scores of the nine factors were calculated by averaging the original item scores. Table 5 shows the means of thenine factors.

To test the research model, the logistic regression technique was run with all nine variables entered in one step. However, thelogistic regression technique is sensitive to multicollinearity. The two-part process was used to diagnose the multicollinearity.

Table 5Means of the all independent variables.

TS FS TC II CP PP CX CM RA

All 3.58 3.36 3.26 3.56 3.06 3.42 2.91 3.21 3.48Adopter 3.56 3.90 3.35 3.23 3.07 3.41 2.39 3.24 3.42Non-adopter 3.59 2.99 3.21 3.80 3.05 3.44 3.28 3.20 3.51

TS: Top management support; FS: Firm size; TC: Technology competence; II: Information intensity; CP: Competitive pressure; PP: Trading partner pressure; CX:Complexity; CM: Compatibility; RA: Relative advantage.

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Table 6Diagnosing multicollinearity.

Condition index Variance proportion

TS FS TC II CP PP CX CM RA

TS 5.47 .00 .22 .00 .00 .01 .00 .03 .00 .00FS 12.19 .00 .19 .11 .00 .01 .17 .41 .01 .01TC 13.75 .00 .02 .06 .00 .06 .30 .14 .14 .02II 14.29 .03 .01 .09 .03 .67 .00 .05 .01 .00CP 15.03 .13 .29 .05 .01 .00 .35 .01 .08 .00PP 16.05 .05 .02 .23 .01 .08 .00 .25 .00 .37CX 17.88 .15 .23 .00 .01 .13 .00 .03 .45 .00CM 22.24 .32 .00 .01 .72 .01 .17 .02 .13 .01RA 23.89 .31 .02 .43 .23 .03 .01 .07 .18 .59

TS: Top management support; FS: Firm size; TC: Technology competence; II: Information intensity; CP: Competitive pressure; PP: Trading partner pressure; CX:Complexity; CM: Compatibility; RA: Relative advantage.

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Hair et al. suggested the two-part process as follows: (1) indentify all condition indices above the 30 threshold, and (2) for allcondition indices exceeding the threshold, identify variables with variance proportion above 90% [60]. As shown in the followingTable 6, no condition index exceeds 30. Therefore, we can find no support for the existence of multicollinearity in theseindependent variables.

Table 7 shows the goodness of fit of the logistic regression model. A well-fitting model has a small value for −2log likelihood(−2LL). A null model using only the mean of the dependent variable provides the baseline for comparison. The −2LL of the nullmodel was 180.38. The −2LL of the research model that consisted of the nine predictors was 84.88. The chi-square test for thereduction in the −2log likelihood value provided one measure of improvement from the null model to the research model: asshown in Table 7, the chi-square test was significant (pb0.001) and the two Pseudo R2 (Cox and Snell R2=0.51, NagelkerkeR2=0.69) proved satisfactory. The research model thus exhibits a good fit with the data.

Table 8 shows how well the research model classified the adopters and non-adopters. The model correctly predicted 83.6% ofthe adopters and 91.0% of the non-adopters, for an overall accuracy rate of 88.0%. These three accuracy ratios exceed the 50% level,ensuring that the prediction model was more accurate than random guessing.

The significance of the regression coefficients of the hypothesized predictors was examined using the Wald statistics todetermine support for the hypotheses. As Table 9 shows, six factors (complexity, compatibility, firm size, competitive pressure,partner pressure, and information intensity) were significant at the 0.05 level. However, relative advantage, top managementsupport, and technology competence were found to be non-significant discriminators. The sign of the regression coefficient (β)represents the positive or negative impact of the independent variable on organizational likelihood to adopt RFID. Therefore, wemay state that (1) compatibility, firm size, competitive pressure, and partner pressure are positively related to organizationallikelihood to adopt RFID, and (2) complexity and information intensity are negatively related to organizational adoption of RFID.

6. Discussion

This study demonstrated the value of using the TOE framework to understand an innovative technology— RFID. The empiricalresults indicated that there were significant determinants in each context of the TOE framework. Thus, determinants affecting the

Table 7Goodness of fit of the model.

Values Significance

−2LLnull model 180.38−2LLresearch model 84.88Change in −2LL 95.50 .001Cox and Snell R2 .51Nagelkerke R2 .69

Table 8Classification table.

Actual Predicted %Correct

Non-adopters Adopters

Non-adopters 71 7 91.0Adopters 9 46 83.6Overall 88.0

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Table 9Results of the logistic regression analysis.

Predictor β coefficient Wald statistics

Relative advantage −.18 .11Complexity −2.52*** 21.34Compatibility 1.34* 4.51Top management support −.26 .27Firm size .65** 6.15Technology competence .08 .03Competitive pressure .91* 3.79Trading partner power .85* 2.56Information intensity −3.52** 22.82

*pb0.05; **pb0.01; ***pb0.001.

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adoption of RFID in the manufacturing industry should include not only the characteristics of the technology itself, but also factorsrelated to the internal organization and the external environment. The discussions about each determinants affecting RFIDadoption were obtained as follows:

6.1. Technology context

Complexity was observed to have a significantly negative influence on RFID adoption in the manufacturing industry. Thephenomenon is also existent in the South African retail sector [33] and the automotive industry [34]. It is certainly more complexto implement than barcode systems [33]. The immaturity of the RFID technology, the lack of common standards, and the difficultyof integrating RFID with the existing enterprise information systems and business processes contribute to the complexity of RFIDadoption [61–63]. Therefore, the complexity of RFID implementation can be an important barrier to RFID adoption.

Compatibility was found to have a significantly positive effect on firm decisions to adopt RFID. The finding is consistent withthat of Brown and Russell [33] and Schmit et al. [34]. If firms’ existing experiences with information systems are compatible withRFID development, RFID applications match existing information infrastructure, and the changes introduced by RFID will beconsistent with existing practices. In that case, a positive impression of RFID is likely to occur and favorably facilitate RFIDimplementation. Therefore, compatibility is positively related to RFID adoption.

Unexpectedly, relative advantage was not found to be a significant discriminator. In fact, some research investigatinginnovation adoption also supported the similar finding [20,38]. In Tornatzky and Klein's meta-analysis of innovation adoption [37],they also found that not all of the studies reported that relative advantage of an innovation was absolutely significantly relevant toits adoption. While relative advantage is not a significant discriminator in this study, this does not mean that the firms think RFIDtechnology has a low level of relative advantage. As shown in Table 5, the average perceived relative advantage levels of RFIDadopters and non-adopters are 3.42 and 3.51, respectively. These two numbers are both above 3.0 (neutral assessment) butslightly different. Nevertheless, since the difference in perceived relative advantage between the adopters and non-adopters isrelatively low, it cannot distinguish between the two groups. This implies that both adopters and non-adopters believe adoptingRFID is beneficial for their companies’ competitive edge. The finding is consistent with that of Fosso Wamba et al. [35] who foundthat firms were interested in benefits such as greater data accuracy, track and trace capabilities, and improved inventorymanagement, regardless of whether they had adopted RFID or not.

Firmsmay not have confidence in the RFID system because it is still in its infancy and relatively new to them [1]. The findings ofthis study suggested that firms seemed to pay more attention to the potential problems or risks of RFID systems (i.e., complexity)than to the potential competitive advantages of RFID systems (i.e., relative advantage) in decidingwhether or not to adopt the newtechnology. As long as firms think that they do not have sufficient technical capabilities to adopt new technology, they wouldrather maintain their current systems [20]. This phenomenon may also make relative advantage an insignificant discriminator ofRFID adoption.

6.2. Organization context

Unexpectedly, the organizational characteristics of top management support and technology competence did not significantlyimpact RFID adoption. There are diverse findings about the effects of these two variables on RFID adoption in the prior studies.Brown and Russell found that topmanagement support and technology competence positively affected RFID adoption in the SouthAfrican retailer sector [33]. However, Leimeister et al. found that the indirect effects of firm's technology competence onwillingness to invest RFID were different across different countries [36]. Furthermore, despite Schmitt et al. pointed out that topmanagement support was an important factor affecting RFID adoption in the automotive industry, only two of their eight citedstudies investigating RFID adoption in the automotive industry mentioned that top management support was an importantdeterminant of RFID adoption and none indicated that firm's technology competence was a significant determinant of RFIDadoption [34]. This result may be due to the fact that RFID is still in its infancy and common standards are lacking [64]. Given thusuncertainty, firms may prefer to wait and see howwell and in what direction RFID technology develops, believing that more cases

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of customer use and validation are required [5]. Thus, top management support and firm's technology competence would beinsignificant discriminator of business RFID adoption in the early stage of RFID industry development.

Firm size emerged as a key variable influencing RFID adoption. This finding is reasonable and consistent with Brown andRussell [33]. Fosso Wamba et al. also found that the firms adopting RFID were less concerned about the acquisition costs,replacement costs, and ongoing costs while those that had not yet adopted RFID were more concerned about these costs [35].Therefore, the cost of hardware, software, consultancy support, installation and integration is an obstacle to RFID adoption[2,61,65,66]. Large firms have greater resources and knowledge to implement RFID, and the economies of scale to derivemaximumbenefit [28,44]. Therefore, firm size has a positive effect on RFID adoption.

6.3. Environment context

Our results indicate that firms adopting RFID perceived significantly higher competitive pressure than non-adopter firms. Thisfinding is consistent with those of previous RFID studies, such as Brown and Russel [33] and FossoWamba et al. [35]. Competitivepressure is an environmental stimulator. When competitors implement RFID as a competitive weapon, firms will feel pressure andbe more receptive to RFID. Thus, RFID adopters are more concerned about the competitive differentiation than non-adopters [35].

Not surprisingly, trading partner pressure was found to be a significant facilitator of RFID adoption in the manufacturingindustry. However, the finding is inconsistent with that of Schmitt et al. [34]. Schmitt et al. referred eight RFID works published in2004–2007 and found that external pressure was not an important RFID adoption factor in the automotive industry. This may bebecause the partner pressure was not huge in some industries some years ago. Nevertheless, many powerful companies ororganizations, such as Wal-Mart, the US Department of Defense, Metro, and Tesco, have recently exerted strong pressure on theirsuppliers to adopt RFID [2,6]. Because of the importance of powerful partners, we can expect that the more pressure from RFIDrequests by trading partners the firms encounter, the more inclined to adopt RFID they are.

Unexpectedly, information intensity has a significantly negative effect on RFID adoption. This finding is inconsistent with thepreviously held notion that firms in more information-intensive environments are more likely to adopt new IT than those in lessinformation-intensive environments [53]. In fact, some previous studies [27,38,55,67] found that the effect of informationintensity on IT adoptionwas not direct, even negative. Because information-intensive products aremore complicated to introduce,manage, and use, they generally require more accompanying information and more complex information processing. Therefore,switching costs and complexities of adopting new IT may be higher in firms with more information-intensive products [38].

Besides, although RFID tags can store more information than bar codes, RFID technology is in its infancy and a significantnumber of implementation questions remain still unanswered [64]. Therefore, some firms in more information-intensiveenvironments may have doubts about whether RFID meets their cost-benefit requirements in effectively supporting information-intensive product processing. These phenomena may cause that firms in more information-intensive contexts would be moreconservative in adopting new RFID technology than those in less information-intensive contexts. However, few studies haveexplored the influence of information intensity on RFID adoption (e.g. [33–36]). Thus, additional research needs to be continuedbefore more concrete conclusions can be drawn.

7. Conclusion

While RFID has been regarded an important technology that can provide strategic and operational advantages, it has yet to seesignificant rates of adoption in the manufacturing industry [2,5]. Hence, it is necessary to understand what determines RFIDadoption in the manufacturing industry. Based on the TOE theoretical framework, this study developed and validated a researchmodel to examine the influence of nine contextual factors on RFID adoption in manufacturing industry. The contributions of thisstudy are fourfold:

First, the study obtains several key findings and implications about the determinants of RFID adoption in the manufacturingindustry. These key findings are as follows. (1) Whether a firm implements RFID in the manufacturing industry depends on thefirm's technological, organizational, and environmental contexts. (2) Six variables (i.e., information intensity, complexity,compatibility, firm size, competitive pressure, and trading partner pressure) were found to be significant determinants of RFIDadoption, but three variables (i.e., relative advantage, top management support, and technology competence) were found to beinsignificant determinants of RFID adoption. (3) Among the six determinants, information intensity and complexity are inhibitorsof RFID adoption, while the remaining determinants are facilitators of RFID adoption. (4) Among the determinants, informationintensity was observed to be the most influential factor affecting a firm's RFID adoption. Complexity was the second mostinfluential predictor of RFID adoption.

Second, this study empirically verifies and supports the applicability of the TOE framework in understanding business ITadoption (i.e., RFID). The TOE framework provides a good starting point for analyzing and considering suitable factors that caninfluence business innovation-adoption decisions. Third, this study found two significant determinants of RFID adoption (i.e.,trading partner pressure and information intensity), which were seldom explored in the prior IT adoption research. Fourth,compared with prior RFID adoption research, this study empirically uses a large and representative sample which consists ofseveral RFID decision makers in the Taiwanese manufacturing industry. Thus, the findings of this study are valuable and provideseveral important implications for RFID adoption research and practice.

This study has several limitations that also represent opportunities for future research. First, since the sample is based on onlyone country, it may not be sufficient to generalize to the entire population of the manufacturing industry in the world.

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Furthermore, because the sampling frame of our study was the list of top-1000 manufacturing firms in Taiwan, these firms mighthave more resources and capabilities to be able to afford RFID investments and risks. For this reason, the RFID adoption rate in oursamplemay be higher than the RFID adoption rate in Taiwanese businesses. Thus, caution needs to be exercised in generalizing ourfindings to the entire industry population in Taiwan or other countries. Samples from different nations or industries should becollected to validate or refine our model.

Second, this study employed the logistic regression technique to identify the predictors that distinguish between adopters andnon-adopters. The technique only focuses on the single relationship between the independent and dependent variables [60].Therefore, the interrelationships among the independent variables (e.g. technology competence may affect complexity) were notanalyzed in this study. Future research can simultaneously examine a series of dependence relationships.

Finally, some hypotheses derived from the TOE model were found to be insignificant in influencing RFID adoption. However,this is not a serious limitation. As Lee noted [68], “falsifiability” is one requirement that a theory must satisfy in order to bescientific. Thus, a theory or model does not necessarily hold in all circumstances. In order for the TOE model to be generalized toother contexts and to allow for new predictions, empirical studiesmust be continuously conducted to validate or revise this model.Besides, many other variables in the TOE model, such as security concerns and government promotion, may be potentialdeterminants of RFID adoption. Future research may incorporate these variables into a predictive model to enhance ourunderstanding of the causality and interrelationships between the predictors.

Acknowledgement

This research was supported by the National Science Council of Taiwan under the grant NSC 98-2410-H-260-020-MY2.

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Yu-Min Wang is an associate professor of Information Management at National Chi Nan University.

Yi-Shun Wang is a professor of Information Management at National Changhua University of Education, Taiwan.

Yin-Fu Yang is a master of Information Management at National Changhua University of Education, Taiwan.