understanding and predicting radio frequency identification (rfid) adoption in supply chains

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Understanding and Predicting RadioFrequency Identification (RFID) Adoptionin Supply ChainsXiaoran Wu a & Chandrasekar Subramaniam aa Department of Business Information, Systems and OperationManagement, The Belk College of Business , University of NorthCarolina , Charlotte, North Carolina, USAPublished online: 07 Oct 2011.

To cite this article: Xiaoran Wu & Chandrasekar Subramaniam (2011) Understanding and PredictingRadio Frequency Identification (RFID) Adoption in Supply Chains, Journal of Organizational Computingand Electronic Commerce, 21:4, 348-367, DOI: 10.1080/10919392.2011.614203

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Journal of Organizational Computing and Electronic Commerce, 21: 348–367, 2011Copyright © Taylor & Francis Group, LLCISSN: 1091-9392 print / 1532-7744 onlineDOI: 10.1080/10919392.2011.614203

UNDERSTANDING AND PREDICTING RADIO FREQUENCYIDENTIFICATION (RFID) ADOPTION IN SUPPLY CHAINS

Xiaoran Wu and Chandrasekar Subramaniam

Department of Business Information, Systems and Operation Management, TheBelk College of Business, University of North Carolina at Charlotte, Charlotte,North Carolina, USA

As an emerging and a promising technology for supply chain management, radio frequencyidentification (RFID) has generated a significant amount of interest from both practition-ers and researchers in recent years. However, the factors important for RFID’s adoption insupply chains have not been well understood. Many organizations are reluctant to partici-pate in supply-chain level RFID projects because of this lack of understanding. Drawing oninnovation diffusion theory and technology-organization-environment framework, we devel-oped a conceptual model for RFID adoption in supply chains. Survey data were collectedworldwide and included different players in supply chains, such as manufacturers, trans-porters, wholesalers, and retailers. Our analysis based on logistic regression demonstratedthat technology complexity, technology maturity, top management support, trading partnerpower, and trading partner readiness were significant predictors for RFID adoption in supplychain activities. This study is the first empirical study to test and validate technology matu-rity as an important factor for technology adoption. We conclude the article by discussingthe theoretical and practical implications of our research findings.

Keywords: logistic regression; radio frequency identification; RFID; supply chain;technology adoption; innovation adoption; technology diffusion

1. INTRODUCTION

Radio frequency identification (RFID) represents a novel approach to tag, track, andmanage entities and is increasingly being adopted for supply chain management. RFIDtechnology can precisely track the location and condition of items as small as a poker chipor as large as a transport vehicle in real time and, hence, offers opportunities to optimizeorganizational supply chains. For example, RFID can improve inventory management andcontrol, help with more accurate production forecasting, reduce losses from counterfeitingand theft, and achieve more timely order fulfillment (e.g., GMA and IBM 2006; Pattonand Hardgrave 2009). RFID technology is likely to affect all aspects of supply chain man-agement (Bose and Pal 2005). In recent years, data analysis techniques have dramaticallyadvanced and provided necessary tools for analyzing the huge amount of RFID data and

Address correspondence to Xiaoran Wu, Department of Business Information Systems and OperationManagement, The Belk College of Business, University of North Carolina at Charlotte, 9201 University CityBlvd., Charlotte, NC, 28223, USA. E-mail: [email protected]

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UNDERSTANDING AND PREDICTING RFID ADOPTION IN SUPPLY CHAINS 349

make it possible to realize RFID benefits. Based on ABI Research in 2010, global RFIDmarket was expected to grow to $5.35 billion in 2010, a 15% increase over the total for2009. The RFID market is predicted to see steady growth over the next five years, reachingmore than $8.25 billion in 2014.

Despite the mandates of retail giants such as Wal-Mart and Target, the adoptionof RFID technology has been much slower than expected. Companies face a series ofchallenges in adopting RFID, especially in their ability to transcend significant techni-cal, managerial, and environmental issues (GMA and IBM 2006; Curtin et al. 2007).Conducting academic research is viewed as an important way to analyze industrial behav-ior in adopting RFID technology. However, there is little research in academic literatureto advance the understanding of the adoption of RFID in supply chains. There are veryfew empirical studies on RFID adoption (e.g., Tsai, Lee, and Wu 2010; Lee and Shim2007), probably because of the difficulties in developing measures and collecting data inthe early stage of RFID diffusion. There is only one academic research paper on RFID thathas been published in a major IS journal until now (Whang 2010), which applies an ana-lytical approach to investigate RFID adoption phenomenon. In sharp contrast, more than athousand articles on RFID have appeared in trade magazines and periodicals since 2000.

Although previous technology adoption research provides some insights to under-stand RFID adoption, the unique features of RFID technology make its adoption differentfrom other technologies. RFID is an emerging and complex technology for supplychain applications. The perceived development status of RFID technology also plays animportant role in organizations’ adoption decisions. In the current business environmenttechnology adoption decisions of an organization may be strongly associated with thebehavior of its upstream or downstream trading partners. RFID can be implemented withina single company and used for supply chain–related activities but it is more likely thatRFID is adopted as an interorganizational technology to exploit RFID’s full potential.However, there is no academic study investigating RFID adoption in supply chains usinglarge-scale data.

The objective of our study is to bridge this gap between the interest in RFID by prac-titioners and the lack of RFID research in academia. We investigate the factors affectingRFID adoption in supply chains by asking the following research question: What factorsare important for the adoption of RFID in supply chain activities?

Our conceptual model is based on Diffusion of Innovation (DOI) theory andTechnology-Organization-Environment (TOE) framework from the innovation diffusionand supply chain management literature. Six factors of RFID adoption were identifiedand six corresponding hypotheses were developed. The hypotheses were tested with datacollected from industry professionals worldwide and our data analysis indicated someinteresting factors that are important for RFID adoption. In terms of the contribution to ISliterature, our work supplements previous innovation research by providing new insightson organizational adoption of a complex technological innovation that needs to integratewith internal and external business processes. Based on our results, we also providedimplications for practitioners.

The rest of our article is organized as follows. The next section presents an overviewof RFID, followed by a discussion of the theoretical foundations, including a review ofrelated literature. The research model and associated hypotheses are then presented, fol-lowed by research method, data analysis, and results. The article concludes with discussionof findings and the contributions of our study for research and practice.

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350 WU AND SUBRAMANIAM

2. AN OVERVIEW OF RFID

RFID technology uses radio frequency waves to exchange data between RFID tagsand readers. A typical RFID system includes three main components: tags, readers, andsoftware. Tags are placed on entities (e.g., items, cases, pallets, or tracks) and store infor-mation about the entities. Readers are used to read and write the data on the tags. Softwarehelps screen and feed RFID data to backend business applications. RFID is used primarilyto locate, identify, track, and trace objects, such as products, containers, and vehicles. RFIDcan not only provide product identification number, price, and cost, but can also includeinformation about the product, such as manufacturing date, location, inventory on hand,etc. More important, RFID information is collected and available in real time. In particu-lar, compared to bar codes (currently the popular technology in supply chain management),RFID does not need a line of sight to read data on the tags; reading from RFID tags can becompletely automatic but bar code needs help from human; RFID can indentify individualitems but bar codes can only indentify classes of entities; RFID data can be more granulardue to the tags applied at individual object-level. These data-rich features make RFID morecomplex than bar codes to integrate into interorganizational decision making.

3. THEORETICAL DEVELOPMENT

3.1. Literature Review

The literature on RFID adoption varies in terms of supporting theories, researchsettings, methodology, and findings. Table 1 provides a summary of our review ofRFID adoption studies. While extant literature on RFID adoption helps understand RFIDadoption, the findings have less generalizability and there are other gaps in the literature.

First, previous studies have used two main dependent variables to capture RFIDadoption. Most studies used “intention to adopt RFID” or “commitment to adopt RFID”as dependent variable in adoption research, which means the companies of respondentshad not adopted the technology yet when those studies took place. Researchers have criti-cized the use of this measure because there can be significant delay between the adoptionintention and an actual adoption action (e.g., Fichman 2000). Moreover, while there is anadoption intention, it does not mean there will be an actual adoption. Actual adoption deci-sion is a different dependent variable capturing real adoption action and this type of studiesis very limited (Chang et al. 2008). However, the current adoption studies can provide evi-dence to distinguish adopters from nonadopters, especially for advanced technologies, suchas RFID, because their adoption processes are complicated and costly. Understanding theactual adoption decision becomes increasingly important.

Second, different theoretical lenses (e.g., DOI theory, theory of technology-push andneed-pull, and institutional theory) have been applied to investigate RFID adoption phe-nomenon in different sectors, such as retailing and health care, and these studies provideinsights into RFID adoption from different perspectives. DOI theory focuses on the impactof the technological characteristics of RFID on its adoption and it fits well with RFIDconsidering the complexity of the technology for supply chain applications. However,DOI theory captures only the technological issues of RFID adoption. Technology-pushand need-pull theory and institutional theory have the similar problem, which addressesonly one aspect (e.g., benefit or environmental issue) of RFID adoption. Innovation adop-tion decision must be studied within appropriate contexts and with variables tailored to the

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Table 1 Selected literature review on RFID adoption.

Study Theory Methodology Factors/Major findings

Bendoly et al.(2007) [DS]

Organizationalinfrastructure

Multisource survey DV: Perceived RFID benefitRFID adoption commitment|ID: Cross-functional knowledge,

procedural flexibilityChang et al. (2008)

[CAIS]TOE Framework Survey Logistic

firms in Taiwan,China

DV: RFID adoptionIV: Uncertainty of environment, degree of

competition in the marketplace,pressure of transaction partners,interorganizational dependency,supplier’s industry environment,organizational scale, fundamentalestablishment of IT, burden of cost,integration of supply chain strategy, topexecutive support and participation,complexity, compatibility, visible profit,visible obstacle, mutual standard

Brown and Russell(2007) [IJIM]

TOE Framework Survey interview6 retailing stores,South African

DV: RFID adoptionIV: Relative advantages, compatibility,

complexity, cost, top managementsupport, IT expertise, firm size,organizational readiness, externalsupport, change agents

Tsai et al. (2010)[I&M]

DOI Theory Survey Retailingfirms in Taiwan,China

DV: Adoption intentionIV: Relative advantage, complexity,

supply chain integration, organizationalreadiness

Lee and Shim(2007) [EJIS]

Theory oftechnology-pushand need-pull

Survey health careindustry

DV: Likelihood of adopting RFIDIV: Performance gap, market uncertainty,

vender pressure, perceived benefit,presents of champions

Whang (2010) [MS] Economics Analyticalmodeling

Technology coordination and cost-splitcontribute to the mitigation of free-riderproblem in RFID adoption.

Whitaker et al.(2007) [POM]

IOS adoption Secondary surveydata from twostudies

DV: Intent to adopt, expected return fromRFID investment

IV: IT budget, IT deployment, RFIDspending, IT integration, partnermandate, RFID standard

Goswami et al.(2008) [ICISProceeding]

Real option Survey Singaporefirms

DV: Intention to adopt RFIDIV: Growth option, learning option,

staging option, deferral option

specific features of the innovation (Chau and Tam 1997). TOE framework provides the con-textual perspectives to analyze organizational adoption of innovations (Jeyaraj, Rottman,and Lacity 2006). RFID adoptions in supply chain activities are driven by its technicalfeatures, organizational factors, and environmental factors, as well as trading partners’cooperation. Hence, TOE framework offers a relatively complete view to examine RFIDadoption issues.

Third, there is little empirical data to characterize worldwide RFID adoption. Mostof the existing literature explores RFID adoption in specific countries or areas, such asSingapore, South Africa, and Taiwan in China. Moreover, these studies focus on only one

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sector, such as retailing. For RFID benefits to be realized to its maximum potential, itshould be used all along supply chains. For example, after Philips Electronics adoptedRFID technology on its product such as razors, the transporter, which provides logisticservices to Philips, could also use the RFID technology to track the products with RFIDtags during transportation process. At the end of the supply chain, wholesalers or retailerscan use the same RFID tags on the products to track their inventory on shelf, back room,or warehouse. However, there are no studies that investigate RFID adoption phenomenoncovering different players in the supply chains. In our study, we attempt to fill this gap inliterature.

3.2. Diffusion of Innovation Theory and TOE Framework

Based on the review, DOI theory and TOE framework provide a more complete viewto investigate RFID adoption from supply chain management perspective and, hence formthe theoretical foundations for our study.

DOI is a fundamental theory to guide technology adoption studies (Rogers 1995).This theory can be used in both individual and organizational levels of technology adop-tion. In the theory, the five most investigated characteristics are: relative advantage,complexity, compatibility, trialability, and observability. Among these, relative advantage,complexity, and compatibility have been consistently identified as important factors topredict innovation adoption.

TOE framework is also widely applied in technology adoption research at the organi-zational level (e.g., Tornatzky and Klein 1982; Chwelos, Benbasatand, and Dexter 2001).TOE framework allows researchers to assess innovation diffusion problem across threedimensions: technological, organizational, and environmental. Technological dimensionincludes both internal and external technology issues associated with a firm. Externally,this dimension deals with how the characteristics of available technologies impact a firm’stechnology adoption activities. Internally, this dimension addresses how a firm’s exist-ing technological base influences its technology choices. The technological dimension inTOE framework is consistent with the DOI theory. Organizational dimension refers toseveral descriptive measures of a firm, such as firm size, the quality of human resources,the amount of available slack resources, managerial structure, and top management strate-gic behavior. Environmental dimension is the arena in which a firm conducts its businessand includes firm’s industry, competitors, access to resources, and government regulations(Tornatzky and Fleischer 1990).

4. RESEARCH MODEL AND HYPOTHESES DEVELOPMENT

Although RFID adoption has been studied from a variety of perspectives, the under-standing of RFID adoption, particularly RFID adoption in supply chain activities, is stilllimited. From a technology diffusion perspective, our study defines RFID adoption as adecision made by an organization to adopt RFID for use in its supply chain activities. Foran IT innovation to be successfully assimilated, adoption stage is a milestone because adop-tion decision legitimizes the resource allocation required to deploy the innovation (Cooperand Zmud 1990). Based on the literature review, an adoption model tailored for RFID tech-nology in supply chains is developed and shown in Figure 1. This model takes into accountthe technological features of RFID in supply chains and the specific organizational andenvironmental characteristics of an organization for RFID adoption.

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Technological Dimension

Environmental Dimension

Organizational Dimension

Technology Maturity IT Sophistication

Top Management Support

RFID Adoption

Trading Partner Power

Trading Partner Readiness

Control Variables

Firm size

H5

H1(-) H2H3

H4

H6

Complexity

Figure 1 RFID adoption research model.

4.1. Characteristics of RFID Technology

4.1.1. Complexity. Complexity refers to the degree to which an innovation is per-ceived as difficult to use (Rogers 1995). Because RFID does not need line of sight to readthe information on RFID tags, it can make it much easier to manage inventory and othersupply chain–related processes. Installing tags and readers for most entities may not bea difficult task. However, RFID implementation in supply chain activities is very compli-cated from an integration perspective (Brown and Russell 2007). Currently, some products,such as HP printers and GAP apparel, are tagged with RFID tags and bar codes together.Existing systems have been designed to store and process barcode data with their ownstructure and transmission. For the coexisting of RFID and barcode data, current systemsneed to be updated for storing and processing RFID data. Moreover, RFID informationneeds to be used for multiple user units (e.g., operation, warehousing, and accounting)and multiple systems (e.g., enterprise resources planning, customer relationship manage-ment, business intelligence systems, etc.) in order to realize its full potential. Given thisnature, it may be difficult to seamlessly integrate and effectively use a large volume ofRFID data with other applications. Hence, implementing RFID may be perceived to becomplex, and when an organization perceives RFID to be complex, it will be less likely tomake a decision to adopt this technology in its supply chain activities. The hypothesis forcomplexity is:

H1: Complexity is negatively associated with RFID adoption.

4.1.2. Technology maturity. Technology maturity could be one of the criticalsources of risk for an adopter’s efforts and expected benefits when an immature technol-ogy is adopted. Any technology should have reached an adequate level of maturity before

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it is widely used in practice. When a technology is perceived as mature, potential adoptersexpect low failure rate, easy availability of related products and services in the market withreasonable and stable prices, and the ability to derive full benefits after its adoption. A com-monly agreed standard for the technology should be built if applicable, especially for itswidespread adoption. Organizations may be reluctant to adopt technologies that are notperceived as mature or stable. Maturity concept has been investigated in studies trying toqualify technology development from a developer side. In IS literature, technology matu-rity concept is rarely investigated in technology adoption research (Wu and Subramaniam2009). Existing IS literature has focused more on the characteristics of technology andignored the impact of technology development status on diffusion. As an emerging andstill-maturing technology, the technology development status of RFID may have a criticalimpact on its adoption.

In our study, technology maturity refers to the degree to which a technology is per-ceived as mature for widespread adoption. While signaling techniques have become moresophisticated to improve the accuracy of reading operations at RFID readers and tags, sta-bility issues still exist, according to industry reports (GMA and IBM 2006). As to RFIDtechnology in the retail environment, stability can be captured by the readability and dataquality, as well as tag failure rates, and these characteristics are identified as key challengesfor current RFID applications (GMA and IBM 2006). As one of the major barriers for thewidespread adoption of RFID, costs have only been partially understood (Fontanella 2003;Roberti 2008). The costs of RFID hardware (i.e., tags) and software (i.e., middleware)are still high (Asif and Mandviwall 2005), although they have fallen and will continue todecrease.

Standards play a critical role in RFID adoption. RFID tags, readers, and backendsystems may be provided by different vendors and common standards are important fordifferent tags to be read by different readers and different RFID systems need to interop-erate with one another. So far there is no global unified standard for RFID technology.Without a commonly accepted standard, it is difficult for firms to communicate, inter-pret, and manipulate information gathered from RFID systems along their supply chains(Markus et al. 2006; Zhu et al. 2006). The perception of RFID as less mature may createa barrier to more aggressive adoption of this technology as companies along the supplychain will be reluctant to adopt until the technology becomes commonplace. Hence, wehypothesize that perceived technology maturity is important factor for RFID adoption.

H2: Technology maturity is positively associated with RFID adoption.

4.2. Organizational Context

4.2.1. IT sophistication. IT sophistication represents the level of IT expertiseavailable within the firm, as well as the level of management’s understanding of IT usageand management (Pare’ and Raymond 1991). Firms with higher IT sophistication usuallypossess a corporate view of data as an important part of entire information managementand have access to required technological resources. The more sophisticated a firm withregard to its IT, the more likely the firm tends to use information technology to achieveits strategic objectives (Bendoly, Citurs, and Konsynski 2007). Firms with highly inte-grated, computerized processes are better prepared to undertake complex IT projects, suchas RFID implementation and integration across the supply chain activities. We expect thathigher IT sophistication will more likely lead an organization to adopt RFID.

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H3: IT sophistication is positively associated with RFID adoption.

4.2.2. Top management support. Top management support refers to the extentto which top management provides long-term strategic vision and necessary resources fortechnology implementation (Sharma, Citurs, and Konsynski 2007). Top management hasthe power to influence other members’ behaviors within the organization and even amongits trading partners along the supply chains. Given the potential of RFID to positivelyinfluence a firm’s competitive position and business relationships, active involvement andsupport from top management provide the appropriate strategic vision and direction besidessending strong signals to the different units of a firm about the importance of RFID tech-nology. Through the long-term strategic vision, top management can encourage the entireorganization to participate in RFID adoption. Also, firms have limited resources and usu-ally many projects including RFID compete for the same scarce resources. With strongsupport from top management, the necessary resources can be allocated to RFID project.Top management support has been consistently identified in literature as a critical factorfor large systems adoption and diffusion (Brown and Vessey 2003). Therefore, our studyhypothesizes that top management support will positively influence firms to adopt RFID.

H4: Top management support is positively associated with RFID adoption.

4.3. External Environment

4.3.1. Trading partner power. Trading partner power measures the impact ofdominant players within a supply chain on their trading partners’ technology adoptiondecision. In the practitioner literature the trading partner power is identified as a majorreason for some firms to adopt RFID systems (GMA and IBM 2006). A study found thatthe main reason for adopting RFID in the warehousing industry was the mandates fromWal-Mart (Vijayaraman and Osyk 2006). Wal-Mart even decided to penalize its supplierswho did not attach RFID tags on the pallets shipped to its Sam’s Club in order to push itssuppliers to adopt this technology (InformationWeek News 2008). These pressures (i.e.,mandates and penalties) from dominant trading partners may promote RFID adoption inthe supply chains. In short, our study expects that, in supply chain contexts, enacted tradingpartner power has a positive impact on adopting RFID.

H5: Trading partner power is positively associated with RFID adoption.

4.3.2. Trading partner readiness. Trading partner’s readiness also needs to beconsidered because it is very likely that implementing RFID system will involve infor-mation gathering and sharing among supply chain partners, which enable the partners tocoordinate and collaborate with one another (Rai, Patnayakuni, and Seth 2006; Chang et al.2008). Trading partner readiness shows whether a firm’s trading partners are ready to adoptand use RFID when the focal firm intends to adopt the technology (Chwelos et al. 2001).This readiness is one of the necessary conditions to maximize RFID benefits. If a tradingpartner is not yet ready to adopt and use RFID system and is also an important node inthe partners’ supply chain network, other partners in the supply chain may not make adecision to adopt RFID as they know they will lose some of perceived benefits from RFIDadoption. Chwelos and colleagues (2001) found that trading partner’s readiness positivelyand significantly contributed to organizations’ intention to adopt EDI. However, it is notclear if the same effect holds for RFID. While RFID adopters must get coordination from

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trading partners to realize the full benefits, RFID can still be implemented within a singleorganization. In the present study, we posit that a firm’s decision to adopt RFID will beinfluenced by the adoption status of its trading partner.

H6: Trading partner readiness is positively associated with RFID adoption.

4.4. Control Variable

Firm size has been consistently recognized as an important factor for technologyadoption. For example, Zhu and Kraemer (2005) found that larger firms are more likely toadopt e-business. Densmore (1998) found that 95% of Fortune 1000 firms had adopted EDIbut only 2% of small companies had adopted EDI. Larger firms may have more resourcesand more experience in information system use, so the benefits to larger firms may besignificantly higher than those to smaller firms (Lee et al. 1999). Hence, in our study, wecontrol for the firm size.

5. RESEARCH METHOD

5.1. Operationalization of Factors

To operationalize the factors in our model, a comprehensive literature review wasconducted. Most constructs and their corresponding measures used in our study have beenvalidated in the literature and we adapted them to RFID context. Since the adoption ofRFID is different from the adoption of other technological artifacts, industry white paperswere also reviewed to tailor existing instruments or create new measures for our study. Theitems for technology maturity are new and specifically developed for this study. Table 2lists the constructs and corresponding measurement items used in the study.

Complexity was operationalized by new items developed in this study. This constructwas measured in current literature by directly asking if a system or technology is complex

Table 2 Items of constructs.

Construct Measurement items

IT Sophisticationa ITS1 IT is important for improving efficiency and productivityITS2 IT is important for effective customer serviceITS3 IT is important for improved competitiveness

Technology Maturity TM1 RFID costs have reduced to a reasonable levelTM2 Commonly agreed standard existsTM3 RFID is stable enough, in terms of readability, failure rate

Complexitya CX1 Involve coordinating with multiple user unitsCX2 Involve multiple technology platformsCX3 Involve integration with multiple other systems

Top Management Supporta TMS1 Top management actively promotes RFIDTMS2 Top management provide stable funding to RFID

Trading Partner Power PP1 Trading partner’s influence on your firm’s RFID adoption decisionb

PP2 The pressure of trading partner on your firm for RFID adoptionc

Trading Partner Readinessd TPR Whether trading partners have adopted RFID

aFive-point Likert-scale: 1 = strongly disagree; 5 = strongly agree.bFive-point scale: 1 = no influence; 5 = very strong influence.cFive-point scale: 1 = no encouragement or pressure; 2 = information exchange; 3 = Recommendation; 4 =

request/promise; 5 = strong pressure.dYes/No.

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or not. Moreover, the results of the impact of complexity on technology adoption are notunequivocal in literature. Hence, we elaborate on the complexity construct and ask respon-dents if RFID implementation involves multiple users units, platforms, and other systems.Higher values of one or more of these items indicate higher complexity. A 5-point Likertscale was used for the items.

Technology maturity is a new factor not well studied in adoption literature, so wedeveloped specific items for this factor in the present study. There are three aspects oftechnology maturity covered in our measures: if the cost for RFID components and serviceshas reached a reasonable and stable level, if there are commonly agreed standards, and ifthere is enough reliability of RFID readings for supply chain applications. A 5-point Likertscale was used.

Top management support was operationalized by items adapted from Wang, Klein,and Jiang (2006). Respondents were asked to indicate the extent to which they agreedor disagreed with statements about their top management’s promotion on RFID and theavailability of stable funding support for RFID project.

Trading partner readiness was coded based on whether the respondents answeredthat their trading partner already has RFID in place in their selected product line. Fortrading partner power, the first item used a scale from “No influence” to “Very strong influ-ence” and the second item was captured from “No encouragement or pressure” to “Strongpressure.” Firm size was measured by the number of employees in respondent’s firm.

RFID adoption was measured by a binary variable: adopter firms or nonadopterfirms. Firms were classified as adopter firms if they specified they have adopted or pilot-tested RFID in a selected supply chain. Otherwise, firms were classified as nonadoptersfor RFID.

4.2. Data Collection

To test our research model, survey data were collected from industry profession-als. A preliminary questionnaire was developed and pilot-tested in two rounds with PhDstudents and industry professionals to assess consistency, ease of understanding, and appro-priateness of the questions. Minor modifications were made to the original questionnaireto clarify the meaning and sequence of some questions. Usually, if a survey focuses on aparticular technology and not on information technology in general, the response rate is rel-atively low. In this study, we obtained 62 responses from the members of the Associationfor Operation Management (APICS) in October 2010. An additional 23 responses wereobtained through contacts in LinkedIn.com, the largest online professional network in theworld, in November 2010. For collecting data from APICS, we sent e-mails with a link tothe survey to a list of professionals obtained from APICS. We followed with two rounds ofreminders with a one week interval. In RFID-related groups in LinkedIn.com, we reviewedthe profile of each member and decided if the member is an appropriate informant for ourresearch. If so, we sent an invitation letter with survey link to the individual respondent.The characteristics of the responded sample are summarized in Table 3.

6. ANALYSIS AND RESULTS

6.1. Measurement Model

In this study, factor analysis and construct reliability are used to test measure-ment properties (Straub 1989). Reliability measures the degree to which items are free

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Table 3 Sample characteristics.

Industry Obs. (%) Country Obs. (%)

Manufacturing 47 55.3% US 53 62.4%Logistics/Transportations 11 12.9% Canada 7 8.2%Warehousing 4 4.7% Europe 12 14.1%Wholesaler/distributor 7 8.2% South America 2 2.4%Retailing 5 5.9% Asia except China 4 4.7%Others 11 12.9% China 1 1.2%

Total 85 100.0 Africa 1 1.2%Others 5 5.9%

Total 85 100.0

from random error and therefore generate consistent results. Reliabilities are reported viaCronbach’s alpha, and the Cronbach’s alpha values for all constructs are above the min-imum threshold of 0.60 (Nunnally 1967), indicating adequate reliability of constructs inour study.

Factor analysis with maximum likelihood extraction, Varimax rotation and KaiserNormalization was conducted and the loadings and factor structure are shown in Table 4.Convergent validity is demonstrated because the items have their greatest loadings ontheir intended construct (loading > 0.50). The item loading on their corresponding con-struct is higher than those on other constructs, thus showing good discriminant validity.Overall our measurement model satisfied the criteria of reliability, convergent validity, anddiscriminant validity.

6.2. Research Model

Logistic regression was used to test the research model because (1) the dependentvariable is binary and (2) logistic regression analysis requires fewer assumptions than otherdiscriminant analysis techniques. This multivariate technique has been used in IS literatureto study open system adoption (Chau and Tam 1997) and EDI adoption (Kuan and Chau2001; Hong and Zhou 2006). The logistic regression results help to predict the change inthe probability that a firm would adopt RFID in its supply chain activities given an increasein each independent variable, when all others are held constant.

6.3. Model Fit

The model fit was assessed in several ways. First, the statistically significant like-lihood ratio test (LR = 60.986, p ≤ 0.001) indicates a strong relationship between thedependent variable and the predictors. Second, the Hosmer-Lemeshow test (X2 = 6.501,p = 0.483) implies that the proposed model is not significantly different from a perfect onethat can correctly classify observations into their corresponding groups (Chau and Tam1997). Third, the variance in RFID adoption explained in the logistic regression model ismoderate, with Cox and Shell R square equal to 0.347 and Nagelkerke R square equal to0.509. The results of logistic regression analysis are shown in Table 5. Our model was ablecorrectly to classify 92.1% of firms who have not adopted RFID and 50.0% of those whohave adopted or pilot-tested RFID in their supply chain activities for an overall prediction

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Tabl

e4

Res

ults

offa

ctor

and

relia

bilit

yan

alys

is.

Fact

or

Con

stru

cts

and

item

sIT

soph

istic

atio

nC

ompl

exity

Top

man

agem

ent

supp

ort

Tech

nolo

gym

atur

ityT

radi

ngpa

rtne

rpo

wer

Cro

nbac

h’sα

ITSo

phis

ticat

ion

ITS1

0.63

30.

034

0.14

−0.0

92−0

.261

0.81

8IT

S20.

819

0.15

10.

040.

054

0.13

1IT

S30.

906

−0.0

610.

188

0.03

10.

111

Com

plex

ityC

X1

0.01

90.

525

0.07

40.

076

−0.0

20.

733

CX

20.

084

0.64

3−0

.001

0.15

9−0

.086

CX

3−0

.001

0.99

4−0

.02

−0.0

45−0

.095

Top

Man

agem

ent

TM

S10.

149

0.06

40.

867

0.12

60.

076

0.85

0Su

ppor

tT

MS2

0.17

20.

034

0.78

70.

184

0.07

5Te

chno

logy

Mat

urity

TM

10.

029

0.02

30.

163

0.46

6−0

.043

0.65

1T

M2

0.03

60.

153

0.02

40.

648

0.02

4T

M3

−0.0

810.

030.

069

0.74

60.

062

Tra

ding

Part

ner

Pow

erPP

10.

02−0

.017

−0.0

49−0

.037

0.89

70.

629

PP2

0.00

3−0

.168

0.21

20.

055

0.52

3

359

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Table 5 Results of logistic regression analysis.

Factor Coefficient Wald statistic Significance Odds ratios

IT Sophistication 0.069 0.023 0.88 1.072Complexity −0.9∗∗ 4.238 0.04 0.406Top Management Support 0.968∗∗ 6.062 0.014 2.632Technology Maturity 0.831∗ 3.317 0.069 2.295Trading Partner Power 0.974∗∗ 5.299 0.021 2.648Trading Partner Readiness 2.872∗∗ 4.739 0.029 17.677Firm Size 0.099 0.541 0.462 1.105Constant −4.33∗∗∗ 8.714 0.003 0.013

Predicted

Nonadopters Adopters % Correct

Observed Nonadopters 58 5 92.1Adopters 11 11 50

81.2

Goodness-of-fitLR statistics: 36.224 Significance: 0.000Hosmer-Lemeshow Chi-square:6.501 Significance: 0.483Cox& Snell R square: .347 Nagelkerke R square: .509−2 Log likelihood: Chi-square: 60.986

∗∗∗ p ≤ 0.01; ∗∗p ≤ 0.05; ∗p ≤ 0.1.

accuracy of 81.2%. As there are 63 nonadopters and 22 adopters, the classification accu-racy by random choice would be (63/85)2+(22/85)2=61.6%. Thus, we conclude that thelogistic regression model has much higher discriminant power than random choice.

6.4. Results

The results of the logistic regression are shown in Table 5. The Wald statistic wasused in the significance test and all independent factors in our model except one hadvalues greater than one (Hauck and Donner 1977). Table 6 summarizes the results ofhypotheses testing. All hypotheses, except Hypothesis 3, are supported by our data. Thestatistically significant, negative coefficient of “complexity” (H1) confirms that complex-ity is an inhibitor of RFID adoption. Technology maturity (H2), top management support(H4), trading partner power (H5), and trading partner readiness (H6) are all found tobe statistically significant and positively associated with RFID adoption. A surprising

Table 6 Results of hypotheses testing.

Hypothesized relationships Result

H1: Complexity is negatively associated with RFID adoption. SupportedH2: Technology maturity is positively associated with RFID adoption. SupportedH3: IT sophistication is positively associated RFID adoption. Not SupportedH4: Top management support is positively associated with RFID adoption. SupportedH5: Trading partner power is positively associated with RFID adoption. SupportedH6: Trading partner readiness is positively associated with RFID adoption. Supported

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result is that IT sophistication (H3) does not reliably differentiate RFID adopters fromnonadopters. In the next section, we discuss the major findings and their implications inmore detail.

7. DISCUSSION AND IMPLICATIONS

7.1. Major Findings and Interpretations

7.1.1. Characteristics of RFID innovation. Consistent with the findings of someprior studies on technology adoption, we find that complexity of RFID significantlyreduced the likelihood of RFID adoption. In other words, compared to RFID adopters,nonadopters perceived RFID to be more complex in terms of integrating with multipletechnology platforms and other systems and involving multiple user units. This impliesthat the lack of capabilities and knowledge to integrate RFID into an organization’s supplychain activities may represent one of the major challenges for organizations to adopt RFID.Other studies have reported that implementing new technology generally requires changesin business processes and changes in the knowledge and skills of employees. When organi-zations perceive a new technology to be more complex, resistance to change would increaseand it will be less likely for the organizations to adopt the new technology. RFID seems tobe similar to other technologies with respect to the impact of complexity on adoption.

Another explanation for the impact of complexity is based on the IS innovationperspective (Swanson 1994). Swanson classified innovations into three classes: Type Iinnovations are technical innovations, related to technical tasks (e.g. CASE); Type II inno-vations use IS to support business administration (e.g., payroll system); and Type IIIinnovations need to be integrated with the core of business where the whole businessis potentially influenced. According to this classification, RFID in supply chain can beconsidered as Type III innovation that aims to generate more efficient and effective busi-ness processes and decision making. Thus, the difficulty of adopting RFID is higher thanother innovations and perceived complexity is likely to be of more importance for RFIDadoption.

While the relationship between technology maturity and RFID adoption had beenignored in previous technology adoption literature (Wu and Subramaniam 2009), our studyempirically tested the impact of perceived technology maturity on RFID adoption studyand found significant support for a positive relationship. When organizations perceive ahigher level of maturity on RFID, they may think that they can gain the benefits fromadopting the technology. Thus, they may be more likely to adopt RFID. In other words,when an organization thinks RFID is not mature enough, there is increased uncertaintyabout the future of the technology. In such situations, the organization may think that RFIDtechnology may not work appropriately in its application environment and adopting thetechnology can put the organization in a risky situation where the current systems may beobsolete and the organizations may need to replace the RFID technology in a short periodof time. These concerns may keep potential RFID adopters away from RFID. At the veryleast, the organization may take a wait-and-see approach for RFID adoption when theyperceive RFID is not mature.

7.1.2. Organizational factors. While previous studies show IT sophistication asan important factor for technology adoption, our study did not find significant support forthe role of this factor in distinguishing RFID adopters and nonadopters. One reason couldbe that the sample in our study happened to have higher levels of IT sophistication, as

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reflected in high mean scores for this construct (mean for adopters = 4.45 out of 5, meanfor nonadopters = 4.34 out of 5, and overall mean = 4.37 out of 5). So the impact of ITsophistication on RFID adopters and nonadopters may not have been discriminated in ourdataset.

In our study, emergence of top management support as a key predictor of RFIDadoption is consistent with other technology adoption studies in literature. A firm has toinitially plan and exert the necessary efforts, such as overcoming suspicions about thetechnology, breaking down any internal resistance, securing necessary resources, andpromoting trust on the part of trading partners, in order to arrive at a decision on RFIDadoption. These activities require the support, intervention, and involvement of seniormanagement of the firm.

7.1.3. Environmental factors. The results show a significant influence of tradingpartner power on RFID adoption. Firms that felt a higher degree of partner power werefound to be more likely to be RFID adopters. As noted earlier, powerful trading partners(e.g., Wal-Mart) place a strong pressure on their partners to adopt RFID in their supplychain activities. Our study provides empirical support to what had often been reported inthe popular press as the reason for RFID adoption.

The study found a strong relationship between trading partner readiness and RFIDadoption. Organizations may clearly know that RFID should be used by all partners inthe supply chain in order to gain its full potential, so they value the adoption status oftheir supply chain trading partners. Another reason for our results regarding trading part-ner readiness may be that an organization whose trading partner has adopted RFID mayfind it easier to learn about RFID and its associated benefits and costs, so it is likely tobehave similar to its trading partner (Burt 1982). Mimetic pressures may also help explainthis finding. Mimetic pressures may cause an organization to change over time to becomemore like other organizations in its environment (DiMaggio and Powell 1983). When anorganization observes its trading partners have RFID in place, the organization will imitatethe actions of trading partners because they stand in the same economic network position(Teo, Tan, and Wei 2003).

7.2. Implications for Research and Management

This study builds on previous adoption studies but is different in important ways.First, the study demonstrates the value of tailoring technology adoption framework tounderstand the adoption of each complex innovation. By focusing on the unique factorsof RFID technology, this study found a group of important predictors for RFID adoption.In particular, the study indicated that a new factor, technology maturity, is important fortechnology adoption studies. While previous studies highlight the role of technology matu-rity when technology developers qualify technology development, the results of the presentstudy recognize the importance of technology maturity from an adopter perspective.Furthermore, we have developed and tested measures in this study for technology maturity,which have passed the reliability and validity tests.

Our study also developed a new set of measures for complexity construct and val-idated them for technology adoption. In literature, this construct is measured by directlyasking questions, such as whether the technology is complex. However we captured RFIDcomplexity in this study by asking if RFID implementation involves multiple units, plat-forms, and user units. These measures are more consistent with the nature of complexity

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construct: an observed score gathered and taken as an empirical analog to the construct andthe elements of scientific discourse.

Second, we collected worldwide data on RFID adoption, including different types oforganizations along supply chains. This increases the external validity and generalizabil-ity of our study, compared to existing literature on RFID adoption. Most RFID adoptionstudies conducted to date have focused on a single country or area. RFID has been used allover the world, so RFID diffusion phenomenon should be investigated in a broader globalcontext.

Third, our study brings an interorganizational perspective to innovation research lit-erature. Since the present study focused on RFID adoption in supply chain activities, thesurvey questions we designed focused on supply chain management related issues, and weasked respondents to consider a product line when they answered the survey questions. Allthese efforts ensure that the results of our study help deepen and broaden our understandingof RFID adoption in supply chain context.

Our study also provides important implications for managers who are interested inadopting RFID technology in their supply chain activities or are involved in efforts tointroduce complex technologies into their organizations. The findings suggest that whenorganizations perceive a technology as more complex, they will not adopt the technologyeven though they may believe their IT to be more sophisticated. One respondent in oursurvey mentioned that “I have seen even large, established companies have senior man-agers, decision makers who do not have any idea of how RFID can improve their lives.”RFID providers should put more efforts on helping organizations understand not only whatbenefits RFID can bring to organizations but also how RFID works in their supply chainmanagement. It is also critical to explain the related complexity for RFID applications insupply chain management. After that, RFID providers need to convince firms that they canovercome the complexity of RFID implementation with the providers’ help.

In addition to validating factors in literature, our findings also encourage managersto evaluate RFID maturity based on their own context. The maturity of RFID is alsodependent on the industry environments and even product types. Our results suggest thatpractitioners consider their own business environment and product types when evaluatingRFID. This finding is consistent with industry practices. In 2010, Wal-Mart’s new initiativeon RFID was to work with suppliers that supply goods with certain attributes to determinehow much value RFID can bring to the supply chain.

The results of our study provide empirical support to the role of trading partnerpower in RFID adoption, especially in retailing supply chains. The study suggests thatusing trading-partner power is a useful way to promote RFID adoption across the supplychain and managers should keep up their pressure on other trading partners to adopt RFID.

Our empirical findings suggest positive network effects—organizations are morelikely to adopt RFID when trading partners are already RFID adopters. As trading partnersadopt RFID, other organizations in the supply chain have more opportunities to conductmore business over RFID if they too have RFID in place, thus leading to higher levels ofbenefits from RFID for all players in the supply chain. RFID vendors, therefore, shouldpay more attention to the potential adopters of RFID whose trading partners already haveRFID in place.

There are some limitations in our study. Our dataset was cross-sectional. We wereable to test associations but not causalities. We also do not know how these relationshipswill change over time. Second, while our study did generate statistically significant results,the dataset is still relatively small compared to studies on EDI. Third, there may be other

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factors which influence RFID adoption in supply chains, but were left out in the presentstudy due to practical limitations of data collection. Factors such as external support andmarket uncertainty may be interesting to study in future research.

8. CONCLUSION

While RFID technology is one of the headlines of industry trade press, it holdsgreat potential to significantly improve supply chain management. However, its adoptionis “much slower than expected” and little research has been conducted to statistically studythis phenomenon. Based on DOI theory and the TOE framework, we proposed a conceptualmodel in this study to improve the understanding of RFID adoption in supply chains. Themodel was empirically examined through data collected from 85 industry professionalsworldwide. Survey data also covered different players in supply chains, such as manufac-turers, transporters, wholesalers, retailers, and others. Our findings indicate that tradingpartner readiness, trading partner power, top management support, technology maturity,and complexity are the predictors of RFID adoption. Among them, trading partner readi-ness, trading partner power, top management support, and technology maturity are thefacilitators for RFID adoption but complexity is an inhibitor for RFID adoption.

For future research, there are several suggestions. First, it would be interesting toexamine a larger group of factors, which may influence RFID adoption and provide a morecomplete view for RFID adoption in supply chain context. For example, external sup-port including training may be important for RFID adoption since it is a complex andnew technology for supply chain applications. One of the managers mentioned in oursurvey, “in developing markets, the use of RFID and associated training also becomes abig challenge.” Future research can also collect more worldwide data to test the adoptionframework. Second, technology maturity has been shown as an important factor for RFIDadoption in our study and it may be interesting to validate the role of this factor in theadoption of other technologies. Third, since adoption is just the first important stage fortechnology diffusion, another interesting direction may be to examine the role of the fac-tors in this study on the other stages of technology diffusion (Cooper and Zmud 1990),such as RFID use. Finally, it would be worthwhile to investigate RFID implementationprocess and its impact on supply chain performance, which could provide a deeper andmore holistic understanding of the consequences and management of RFID technology.

ACKNOWLEDGEMENTS

The authors gratefully acknowledge the support received from APICS for datacollection.

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AUTHOR BIOS

Xiaoran Wu is a PhD candidate in the Business Information Systems and OperationManagement Department at the Belk College of Business, University of North Carolina atCharlotte. She holds a B.S. in Chemical Engineering from Beijing University of ChemicalTechnology. Before joining the PhD program, she worked for several years in a varietyof industries. Her research interests are technology diffusion, technology usage and ITvalue, and IT-enabled supply chain management. Her research has been presented at theHawaii International Conference on System Sciences, the Workshop on e-Business, and theWorkshop on Enterprise Systems Research. She is also an active member in the Associationof Information Systems and Council of Supply Chain Management Professionals.

Chandrasekar Subramaniam is an Associate Professor in the Department of BusinessInformation Systems and Operations Management at the Belk College of Business,University of North Carolina at Charlotte. He received his PhD from the University ofIllinois at Urbana–Champaign. His research interests include electronic commerce ande-business, value of information technology, interorganizational systems, open sourcesoftware, and IT security. He has published in Decision Support Systems, Journalof Management Information Systems, International Journal of Electronic Commerce,Communications of the AIS, and Information Systems Frontiers.

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understanding and predicting radio frequency identification (rfid) adoption in supply chains

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