Structural equation modeling for multi-stage analysis on Radio Frequency Identification (RFID) diffusion in the health care industry

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Abstract

Structural equation analysis for multi-stage analysis on Radio Frequency Identification (RFID) diffusion in the health care industry.

Faced with an increasingly competitive business environment, organizations in the health care industry are applying Radio Frequency Identification (RFID) to improve operational efficiency and to gain a competitive advantage over their competitors. This research provides a multi-stage analysis on the antecedents that affects the diffusion of RFID in the health care industry. Data collected from 182 health care organizations were analyzed using structural equation modeling analysis. The result shows that variables within the Technology-Organization-Environment (TOE) framework and the Diffusion of Innovation (DOI) theory have different effects on the evaluation, adoption, and routinization stages of RFID diffusion. This is one of few empirical studies on the factors influencing the diffusion of RFID in the health care industry. The results of this study will help decision makers in the health care industry to better understand the diffusion process of RFID, and to formulate strategies for successful diffusion of RFID.

Highlights

► We model the diffusion of RFID in health care industry. ► SEM was applied to examine the model. ► Variables from TOE and DOI models have different effects on RFID diffusion stages. ► Decision makers can formulate appropriate RFID strategies from the findings.

Introduction

The health care industry is currently one of the fastest growing industries (Curry and Sinclair, 2002, Hegde, 2008). The health care industry is also facing many challenges from the increasingly competitive and globalized business environment (Tsacle & Aly, 1996). In order to stay competitive, businesses in the health care industry have applied new technologies to manage patients, personnel, and inventory to streamline the efficiencies and effectiveness of business functions (Fisher & Monahan, 2008). One technology that has gained attentions from the health care professions is Radio Frequency Identification (RFID) system. RFID is the generic name for technologies that use radio waves to identify and track objects (Jones, Clarke-Hill, Shears, Comfort, & Hillier, 2004). RFID is traditionally applied to improve the supply chain of an organization (Lin, 2009). By implementing RFID system, objects can be automatically recognized, identified, tracked and traced from the factory, shipping, warehousing, hospitals, pharmacies, intermediaries, and customers (Kumar et al., 2009, Poon et al., 2009). Although traditionally applied in the manufacturing industry, RFID have now being applied in industries such as health care, life sciences, transportation and the government (Lai, Hutchinson, & Zhang, 2005).

The applications of RFID in the health care industry have much potential. RFID can help to improve the current stock management systems in hospitals and clinics. For example, there are many equipments and medicines that are involved in the management of inventories in hospitals. Hospitals need to keep track of whether they have enough medicine, the expiry dates of medicines, whether the medicines are given to the right patients etc. In many hospitals, certain drugs also need to be kept and prescribed carefully as it needs doctors’ special prescriptions. Other applications of RFID include putting an RFID tag to patients to identity and keeping patients records and treatment needs accurately. Health care businesses such as hospitals and clinics have traditionally relied on manual paper process to manage their operations. However, most health care organizations today use information systems to manage their business operations. Although traditional hospital information systems have improved health care industries’ operations, there are still many benefits that can be realized by implementing RFID. Through RFID, health care businesses can improve their organizational performance and competitiveness (Lim and Koh, 2009, Loebbecke and Palmer, 2006) besides operations improvements, RFID can also help improve patients’ safety (Vanany & Shaharoun, 2009).

RFID has also gained attentions from the health care sector in Malaysia. Health care business is one of the fastest growing service industries in Malaysia (Chee & Barraclough, 2007). The Malaysian government has actively promote Malaysia as a destination for medical or health tourism, whereby its high quality medical service and affordable costs are able to attract many customers from different countries. Besides competitions between Malaysian hospitals and clinics for businesses, the Malaysian health care industry is also competing with countries such as Singapore and Thailand (Chee & Barraclough, 2007). Besides offering good medical services to compete, one way in which the Malaysian health care business can compete is to be more efficient and effective in their operations (Chang, 2007). Although RFID is able to help to achieve this, many hospitals for example, are still reluctant to implement RFID (Vanany & Shaharoun, 2009). During the outbreak of bird flu, Singapore’s hospitals ensured the safety of medical staffs and patients by identifying and tracing possible-infected individuals. The efforts and time to develop the history of patients’ contacts with each other is huge, but RFID was able to help improve this process. As such, the Singapore health care industry adopted RFID, while Malaysia’s health care industry chose not to adopt RFID to solve this problem (Vanany & Shaharoun, 2009). Nevertheless, not all health care companies and hospitals are resisting the adoption of RFID. Pantai Hospital, a private hospital in Malaysia, used RFID for patient temperature monitoring, and location tracking (Edwards, 2010). Despite the benefits reported by Pantai hospital, the adoption of RFID in the health care industry in general, remain low ((Kumar et al., 2009, Vanany and Shaharoun, 2009). RFID is also currently widely available, and it is not difficult to integrate RFID into the health care supply chain (Riggins & Hardgrave, 2007). Past literatures have attempted to investigate the factors that affect the adoption of RFID (Brown and Russell, 2007, Mehrjerdi, 2010, White et al., 2008). However, these studies did not propose an empirical model that examines the factors that can affect the implementation of RFID. Among those who did focused on proposing such model such as those conducted by Lee and Shim, 2007, Madlberger, 2009, Matta and Moberg, 2007, Tsai et al., 2010, their models have specifically examined a single stage adoption (e.g. whether organizations are adopting or not adopting RFID). There is little knowledge on the various stages of RFID diffusion, especially in the health care industry. Existing technology studies from Zhu et al., 2006, Wu and Chuang, 2010 have stated the needs to conduct such multi-stage, diffusion studies to understand the process of why organizations choose to implement a technology across time. In order to bridge the gap in existing RFID adoption literature, this research aims to study on the factors that affect the diffusion of RFID in the health care industry, from an organization’s decisions to adopt, deploy and assimilate RFID.

Section snippets

RFID overview

RFID is the generic term for systems and technologies that use “radio waves to transmit and automatically identify people or objects” (Sharma, Citurs, & Konsynski, 2007). Although RFID was developed in the early 1970s, it has only gained the attentions from both academics and practitioners recently (Mehrjerdi, 2010). One reason for the recent interest in RRID is due to the decreasing costs of RFID (Sharma et al., 2007). A RFID system will usually have three components: tags, readers, and

Research model

Fig. 1 shows the conceptual model developed for this research. There are three dependent variables in this research: evaluation, adoption and routinization. The independent variables in this research are the technological, organizational, and environmental factors. The research model proposes that the three factors will have significant relationships with the three stages of RFID diffusion.

Technological factor

Relative advantage, compatibility, complexity, cost and security are five attributes that are specified

Data

A survey instrument was developed to test the hypotheses proposed in this research. The survey was reviewed with a hospital manager and a clinic operations manager in Malaysia to ensure that the wordings and formats were appropriate for the health care industry. The surveys were requested to be completed by the operations manager, IT managers, or logistic mangers of the health care companies and hospitals. In order to reduce the biasness in the survey collected, we informed respondents that

Discussions

This study empirically examined the effects of technological, organizational, and environmental factors on the three stages of RFID diffusion in the health care industry. The results will be discussed below as framed by the TOE context.

Technological factor In general, technological variables derived from the DOI model (e.g. relative advantage, compatibility, and complexity) only have positive relationships with certain RFID diffusion stages. Relative advantage for example, is only found to be

Conclusions and implications

More organizations in the health care industry are implementing RFID in their business operations. Based on the Malaysian health care industry, this research has applied the TOE and DOI models, and empirically investigates the factors that affect the diffusion of RFID. This multi-stage analysis provides a better understanding of the dynamic nature of RFID diffusion in the health care industry. This research reports several important findings. Firstly, it is found that although variables from

Limitations and future studies

This study has several limitations and opportunities for future study. Firstly, the data for this research is based on the Malaysian health care industry. It would be interesting to collect data from other nations and conduct a cross country comparison study in the future. Secondly, this research has focused specifically on the health care industry. Future studies can consider targeting other industries (e.g. manufacturing or electronics industry), and compare the results found in other

Acknowledgements

The work described in this paper was substantially supported by a grant from the Hong Kong Polytechnic University Postdoctoral Fellowships Scheme (Project No. G-YX4D). The authors would like to thank Hong Kong Polytechnic University Research Committee for the financial support.

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