Predicting E-procurement adoption in a developing country: An empirical

integration of technology acceptance model and theory of planned behaviour

Mohamed Gamal Aboelmaged

Associate Professor and Head of Management Department,

College of Business Administration, Ajman University of Science and Technology,

United Arab Emirates

gaboelmeaged@yahoo.com

P.O. Box 346, Ajman, United Arab Emirates

Brief professional biography

M. G. Aboelmaged has a PhD in Management Science from Lancaster University, UK, and MA

in Public Policy & Administration from the Institute of Social Studies, The Netherlands.

Currently, He is an Associate Professor of Management at Ain Shams University, Egypt and

Ajman University of Science and Technology, United Arab Emirates, where he chairs the

Management Department. His research interests include technology adoption and

implementation, enterprise systems, quality systems, e-business, and supply chain management.

His work has been published in international journals and conference proceedings. He also gives

professional training and consultancy regularly.

Predicting E-procurement adoption in a developing country: An empirical integration of

technology acceptance model and theory of planned behaviour

Abstract

Purpose – This paper predicts E-procurement adoption through integrating the constructs

of the technology acceptance model (TAM) and the theory of planned behaviour (TPB).

Design/methodology/approach – A structural equation modelling (SEM) is conducted

through the analysis of 316 usable questionnaires.

Findings – The results show that the proposed model has good explanatory power and

confirms its robustness, with a reasonably strong empirical support, in predicting users’

intentions to use e-procurement technology. Behavioural intention toward e-procurement

technology is mainly determined by user’s attitude and additionally influenced by

perceived usefulness and subjective norm.

Practical implications: The paper provides procurement system developers and mangers

with a useful adoption model that demonstrates the significance of perceived usefulness

of e-procurement system in influencing the adoption decision. This highlights the

importance of maximizing the benefits of e-procurement system for potential users to

facilitate the adoption process.

Social implications: System developers and procurement mangers should also consider

the role of social influences, such as these from supply chain partners, in the adoption

process and how such influences may facilitate or inhabit e-procurement adoption

process.

Originality/value – The paper is the first study that examines e-procurement adoption in

the United Arab Emirates. Also, the findings allow us to understand the importance of

both technology-related aspects and social influence in e-procurement adoption.

Keywords E-procurement, technology adoption, technology acceptance model, theory of

planned behaviour, United Arab Emirates

Paper type Research paper

Predicting E-procurement adoption in a developing country: An empirical integration of

technology acceptance model and theory of planned behaviour

Abstract

Purpose – This paper predicts E-procurement adoption through integrating the constructs of the technology acceptance model (TAM) and the theory of planned behaviour (TPB). Design/methodology/approach – A structural equation modelling (SEM) is conducted through the analysis of 316 usable questionnaires. Findings – The results show that the proposed model has good explanatory power and confirms its robustness, with a reasonably strong empirical support, in predicting users’ intentions to use e-procurement technology. Behavioural intention toward e-procurement technology is mainly determined by user’s attitude and additionally influenced by perceived usefulness and subjective norm. Practical implications: The paper provides procurement system developers and mangers with a useful adoption model that demonstrates the significance of perceived usefulness of e-procurement system in influencing the adoption decision. This highlights the importance of maximizing the benefits of e-procurement system for potential users to facilitate the adoption process. Social implications: System developers and procurement mangers should also consider the role of social influences, such as these from supply chain partners, in the adoption process and how such influences may facilitate or inhabit e-procurement adoption process. Originality/value – The paper is the first study that examines e-procurement adoption in the United Arab Emirates. Also, the findings allow us to understand the importance of both technology-related aspects and social influence in e-procurement adoption. Keywords E-procurement, technology adoption, technology acceptance model, theory of planned behaviour, United Arab Emirates Paper type Research paper

Introduction

The growing emphasis on managing the supply chain has drawn managers’ attention to the valued-added potential of Internet technology to achieve better information, improve bottom-line costs, emphasis on time to market, and maximize procurement effectiveness (Carayannis and Popescu, 2005; Roth, 2001; Presutti, 2003; Puschmann and Alt, 2005). Procurement is considered as a strategic player in the value chain as it usually represents one of the largest expense items in a firm’s cost structure. According to Hawking et al., (2004), the purchase of goods and services represents the single largest cost item for any given enterprise since each dollar a company earns on the sale of a product; it spends about $0.50 to $0.60 on goods and services. Further, more capital is spent on the purchase of materials and services to support the business’s operations than on all other expense items combined (Hawking et al., 2004). The Aberdeen Group (2001) in a study of spending analysis practices of 157 firms revealed that only a few firms truly know and understand how much they spend, on which products, and with which suppliers. Moreover, Turban et al. (2006) indicated that purchasing officers in manual or even slow systematic procurement transactions tend to waste time on non-value adding activities

such as data entry, expediting delivery, solving quality problems, and handling errors in ordering, costing and invoicing; which were often time consuming and costly to trace. Traditionally, procurement has involved a number of communication mediums including the use of mail, phone, fax, electronic data interchange (EDI) and more recently, email and the internet to facilitate procurement process between the various parties. The unique features of the Internet and related web-based technologies can potentially support and improve the activities of procurement process through transforming traditional paper-based processes to e-procurement (Gebauer et al., 1998). However, e-procurement has been defined in a number of ways. Along with its advancement, e- procurement definition has evolved to mean:

� ‘‘Internet solutions that facilitate corporate purchasing’’ (Alaniz and Roberts, 1999).

� ‘‘A series of steps—from the formulation of the purchasing corporate strategy to the actual implementation of an Internet-based purchasing system’’ (Morris et al., 2000)

� “Automating the whole purchasing process and making order and requisition information available along the entire supply chain” (Roche, 2001).

� ‘‘The creation of private, web-based procurement markets that automate communications, transactions and collaboration between supply chain partners” (Aberdeen Group, 2001).

� “Various Internet based B2B (business-to-business) commerce (trading or buying-and-selling) systems, which are located at the buyer, the supplier or the third party” Kima and Shunk (2004)

� ‘‘The integration, management, automation, optimisation and enablement of an organization’s procurement process, using electronic tools and technologies, and web-based applications’’ (Tatsis et al., 2006).

� “The use of information technologies to facilitate B2B purchase transactions for materials and services” (Wu et al., 2007).

Although these definitions vary in scope and detail, it is clear that e-procurement implies the use of information and communication dimensions of Internet technology for obtaining materials and services and managing their inflow into the organization. Operationally, e-procurement involves six forms of activities, including e-ordering/e-Maintenance Repair Operate (MRO), web-based enterprise resource planning (ERP), e-sourcing, e-tendering, e-reverse auctioning/e-auctioning and e-informing (de Boer et al., 2002). These activities are applicable to the three e-procurement categories which were classified by Aberdeen Group (2001), including: - Direct procurement that includes the procurement of raw materials, parts and assemblies; - Indirect procurement that includes the procurement of non-production goods and services such as office supplies, printing, advertising and casual labor; and - Sourcing which involves identification, evaluation, negotiation of products and supplies for both the indirect and direct supply chain. E-procurement has been evolved to the point where the goal is not to make a supplier drop their prices or lower their margins but to achieve savings, which can be realized by both buyers and vendors, through managing material and administration costs (Kothari et al., 2005). E-procurement has cut down the time and cost required to generate a purchase order, place the

order, determine the nature of contracts, select the right supplier(s), track shipment status, manage payments, and follow up with supplier(s). Presutti (2003) stated that the move to e-procurement provides supply managers with a unique opportunity for two reasons. First, boosting competitiveness and profitability through technology application is on the agenda of any forward-thinking CEO. Second, attention is given to analyze where the firm spends most operating dollars. Researchers are optimistic on the level of savings that can be achieved through full implementation of e-procurement strategies. For instance, General Electric believes that it has saved over $US 10 billion annually through its e-procurement activities (Hawking et al., 2004). Moreover, companies such as Motorola, Nestles, Renault, and Schlumberger have a long history of using e-procurement as a competitive strategy that leads to vast reduction in cost and time throughout procurement phases (Yu et al, 2008). The organizational decision to adopt e-procurement is usually taken by boards and managers who take information about both the alternatives and the consequences into account (Batenburg, 2007). Nonetheless, the adoption decision neglects important issues such as user acceptance of e-procurement (Batenburg, 2007; Bouwman et al., 2005). To set one important scope of this study, we focus on understanding the role of factors that might influence the intention to use e-procurement system. Although several studies have focused on the factors that impact on the adoption of internet-based technology for the past decade (Heijden, 2003; Hu et al., 1999; McKechnie et al., 2006; Lederer et al., 2000; Pavlou, 2003), there is limited empirical work on e-procurement adoption to help form a strategic agenda (Aberdeen Group, 2000; Kothari et al., 2005). Therefore, this study provides a solid theoretical framework for examining the adoption of e-procurement drawing on two schools of thought: (1) the theory of planned behaviour (TPB) (Azjen, 1991) and (2) the technology acceptance model (TAM) (Davis et al., 1989). TAM and TPB have been used in many studies to predict and understand the adoption of an online system (Chen et al., 2007; Lee, 2009; Gefen et al., 2003; Wu and Chen, 2005). Through integrating TPB and TAM, this study provides a more comprehensive model of e-procurement adoption. Specifically, the objective of this study is to investigate the factors that influence the intention to use e-procurement system through integrating TPB and TAM models. This study would contribute to the theoretical development of behaviour formatting toward e-procurement adoption. Results of the study can provide practical implications for e-procurement system developers and purchasing managers to plan strategically and implement effective tools to motivate purchasing employees toward actual use and acceptance of e-procurement system. The paper proceeds as follows: second section introduces a background of the UAE context. Third section is devoted to the theoretical framework for this study. Fourth section outlines the development of research hypotheses. Research methodology and design are presented in section five, while section six provides the data analysis and hypotheses testing results. Section seven discusses our research findings, and finally section eight concludes with this paper’s limitations, and potential topics for future research. Background of the UAE context

The United Arab Emirates is a Middle Eastern country situated in the southeast of the Arabian Peninsula in Southeast Asia on the Persian Gulf, comprising seven emirates: Abu Dhabi, Dubai, Sharajah, Ajman, Ras al-Khaimah, Fujairah and Umm al-Quwain. The economy of United Arab Emirates (UAE) is largely dependent on oil and gas production. It became a highly prosperous

country after foreign investment began funding the desert-and-coastal nation in 1970s. Accordingly, the UAE has witnessed a magnificent standard-of-living increase in the last three decades resulted from oil revenues. With a relatively small area (83,600 sq km), the population has reached 4.7 million as estimated in 2006 and the UAE's per capita GDP is on par with those of some West European nations ($27,610 in 2006) (ESCWA report, 2006). To maintain the impressive growth, the UAE went for a large-scale technology transfer and adoption to be one of the most technologically sophisticated countries in the Middle East. At the heart of the growing information technology market, the UAE IT sector grew from US$ 6.9 billion in 2003 to more than US$ 9.5 billion in 2005 (ESCWA report, 2006). Identified as the regional ICT hub in the region, Dubai is a leading city in adopting technology. The Dubai Technology E-Commerce & Media Freezone (TECOM), which was established in 2000, has a total of 680 companies (from IT and Telecom sectors). TECOM is also home to Dubai Internet City, Dubai Media City, and Dubai Knowledge Village. The UAE has fared very well on the front of technology according to the World Economic Forum’s Arab World Competitiveness Report 2007 (The Arab World Competitiveness report, 2007). Information communication technology infrastructure is very well developed in the UAE as evidenced by its ranking (32nd. Globally) on the Information Society Index (ISI) based on four categories including (1) Computer Infrastructure; (2) Internet Infrastructure; (3) Information Infrastructure; and (4) Social Infrastructure (Shalhoub, 2006). E-business transactions in the UAE are progressing at a slow rate. While the trading platform recorded more than $ 3 billion as on December 31st., 2006 in transactions since its founding in 2000, this represents a small percentage of intra-regional trade (ESCWA report, 2006). However, the Ministry of Finance and Industry started to offer online services to its customers and the public through e-procurement service, where users can register, select the service, apply, fill in the forms, upload the documents and pay online. To support this service, the government gateway provides clear access to two excellent sites one of which is e-Dirham portal, e-dirham.gov.ae, for online transactions and payment, and the e-Forms portal, uaesmartforms.com, for online forms and documents. These services encourages Dubai World to launch the e-commerce marketplace Tejari.com, and now franchised in neighbouring countries (i.e., Oman, Jordan, Saudi Arabia, Kuwait, Lebanon, and Pakistan). The introduction of all the above services has created awareness on the importance of e-business applications for making business processes more efficient and effective. Theoretical framework

Literature provides a considerable amount of academic research examining the determinants of IT adoption and utilization among users, (e.g. Venkatesh, 2000; Hsu and Chiu, 2004). Theory of planning behaviour (TBP) and technology acceptance model (TAM) are among these models that have gained attention and confirmation in a wide array of areas and applications to understand end-user’s intention to use new technology and systems (Armitage and Conner, 2001; Venkatesh and Davis, 2000). However, TBP and TAM were developed as an extension to Ajzen and Fishbein’s (1980) theory of reasoned action (TRA). TRA is conceived as a general structure designed to explain almost all human behaviour and is based on the importance of an individual’s beliefs for the prediction of his/her behaviour (Fishbein and Ajzen, 1975; Ajzen and Fishbein, 1980). According to TRA, behavioural intention to exhibit a particular behaviour is formed based on the individual’s attitude toward the behaviour and on perceived subjective norm. The first determinant, attitude toward behaviour, reflects a person’s beliefs that the behaviour leads to certain outcomes and the person’s evaluation of those outcomes, favourable or unfavourable. The more positive the attitude, the stronger the behavioural intention and, ultimately, the higher the

probability of a corresponding behaviour should be. The second determinant is subjective norm which captures individual’s perceptions of the extent to which his social environment (e.g., family, friends, co-workers, authority figure, or media) influences such a behaviour to be normal and desirable. The more strongly this pressure is experienced, the greater the behavioural intention and, indirectly, the probability that the behaviour will be realized. Ajzen (1987; 1991) and Ajzen and Madden (1986) developed the TRA further into TPB by adding new determinant of behavioural intention, perceived behavioural control, which is based on Bandura’s concept of self-efficacy. Perceived behavioural control assesses the degree to which people perceive that they actually have control over enacting the behaviour of interest. It is suggested that individuals are more likely to engage in behaviours they feel to have control over and are prevented from carrying out behaviours over which they feel to have no control. As a result, a person who does believe himself capable of certain behaviour will exhibit correspondingly a behavioural intention to exhibit a particular behaviour. According to TPB, the more favourable the attitude and subjective norm with respect to a behaviour, and the greater the perceived behavioural control, the stronger should be an individual’s intention to perform the behaviour under consideration (Ajzen, 1987; 1991) (see Figure 1). Most empirical applications of the TPB try to explain or predict newly introduced behaviour (Armitage and Connor, 2001). The second theoretical grounding for this research is derived from the technology acceptance model (TAM) which is initially developed by Davis (1989) and Davis et al., (1989) as an extension of Ajzen and Fishbein’s theory of reasoned action (TRA) to explain and predict particularly information technology (IT) usage behaviour across a wide range of technologies and user populations. TAM has received much attention from researchers and practitioners as a parsimonious yet powerful model for explaining and predicting usage intention and acceptance behaviour (Yi and Hwang, 2003). In contrast to TRA and TPB models, TAM focuses exclusively on the analysis of information technology (Chau, 1996; Venkatesh, 2000; Mathieson et al., 2001; Childers et al., 2001; Featherman and Pavlov, 2003). However, the topics of TAM research have been varied, including the employment of personal computers in the workplace (Hamner and Qazi, 2009; Moore and Benbasat, 1991; Igbaria et al., 1996), internet use (Lederer et al., 2000); e-commerce (Pavlou, 2003); ERP acceptance (Amoako-Gyampah and Salam, 2004); telemedicine (Hu et al., 1999); internet banking (McKechnie et al., 2006), and mobile Banking (Luarn and Lin, 2005). Mathieson et al. (2001) argued that TAM’s ability to explain attitude toward using an information system is better than the other multi-attribute models’ such as TRA and TPB. Venkatesh and Davis (2000, p. 186) note ‘‘TAM consistently explains a substantial proportion of the variance (typically about 40%) in usage intentions and behaviour and that TAM compares favourably with alternative models such as the Theory of Reasoned Action and the Theory of Planned Behaviour’’. In turn, attitude in TAM is influenced by a priori two key elements determining technological behaviour: perceived ease of use (PEOU) and perceived usefulness (PU) (Davis, 1989; Igbaria et al., 1996). Davis (1989, p. 320) defined perceived usefulness as the degree to which “a person believes that using the system will enhance his or her performance” and perceived ease of use as the degree to which “a person believes that using the system will be free of mental effort”. According to TAM, perceived usefulness and perceived ease of use both affect a person’s attitude toward using the system, and consistent with TRA, these attitudes toward using the system determine behavioural intentions, which in turn lead to actual system use.

Although TPB and TAM have been widely applied to examine adoption and acceptance of information technology, neither TPB nor TAM has been found to provide consistently superior explanations or predictions of behaviour (Chen et al., 2007; Taylor and Todd, 1995; Venkatesh et

al, 2003). This may be due to the various factors that influence technology adoption, type of technology and users, and the context (Chen et al., 2007). Consequently, a growing body of research has focused on integrating TPB and TAM to examine technology adoption owing to the complimentary and explanatory power of the two models together (Chau and Hu, 2002; Chen et

al., 2007; Hung et al. 2006; Lu et al., 2009; Wu and Chen, 2005). Since the focus of this study is e-procurement adoption, the integration of TPB and TAM constructs for our research model should provide strong empirical support to e-procurement adoption research and account for the technological and social factors influencing the intention to use e-procurement system. Development of research hypotheses

According to TAM and TPB, attitude toward using a particular system is a major determinant of the intention to use that system, which in turn generates the actual usage behaviour. The underlying premise is that individuals make decisions rationally and systematically on the basis of the information available to them (Ajzen, 1991). Many existing studies in the context of e-business have shown that individual’s attitude directly and significantly influences behavioural intention to use a particular e-business application (e.g., George, 2002; Gribbins et al., 2003; Moon and Kim, 2001). For example, George (2002) found a strong positive relationship between an individual’s attitude toward purchasing online and the user’s behavioural intention. Gribbins et al. (2003) studied the acceptance of wireless technologies by users. They demonstrated support for the relationship between attitude toward using mobile commerce and behavioural intention. Thus, the following hypothesis is proposed:

H1. Attitude will have a positive effect on the individual’s intention to use e-procurement system.

Consistent with TPB, subjective norm captures the pressure of social environment ingredients such as family, friends, co-workers, authority figure, or media on behaviour to be normal and desirable. The more strongly this pressure is experienced, the greater the behavioural intention and, indirectly, the probability that the behaviour will be realized. Existing research have found a significant relation between subjective norm and intention in online settings. For example, Battacherjee (2000) found a positive impact of subjective norm on intention to use electronic brokerages service. Venkatesh and Davis (2000) established direct link between subjective norm and intention to use in a study pooling results across four longitudinal field studies. In addition, Liao et al. (2007) developed an integrated model to predict individual's use of online services based on the concepts of the expectation disconfirmation model and the theory of planned behaviour. The findings showed that subjective norm is a strong determinant of behavioural intention towards e-service. Consequently, we propose the following hypothesis:

H2. Subjective norm will have a positive effect on the individual’s intention to use e-procurement system.

Similarly, previous research in online technology adoption suggested perceived behavioural control as a good predictor of usage intention (Choi and Getsfield, 2003; George, 2002; Klein

and Ford, 2003; Shim, 2001). A user who does believe him/herself capable of using such an e-business application as e-procurement will exhibit correspondingly a behavioural intention to use that application. Shim et al. (2001) predicted perceived behavioural control would positively impact behavioural intention of users to search online. Moreover, George (2002) suggested that perceived behavioural control has a direct effect on the user’s attitude toward using the internet for online purchase. Based on the foregoing argument, this study examines the following hypothesis:

H3. Perceived behavioural control will have a positive effect on the individual’s intention to use e-procurement system.

Lai and Yang (2009) argue that employees in a performance-oriented e-business context are generally reinforced for good performance and benefits. This implies that realizing usefulness of e-business applications such as e-procurement in improving performance or efficiency will positively impact attitude toward that application. The effect of perceived usefulness on attitude has been validated in many existing studies including (e.g., Chen et al., 2002; Cheung and Liao, 2003; Gribbins, et al., 2003; Heijden et al., 2003; Liao and Cheung, 2001). Therefore, the following hypothesis is advised:

H4. Perceived usefulness will have a positive effect on the individual’s attitude toward e-procurement system.

Further, perceived usefulness can lead to behavioural intention. This proposition is justified from the perspective that people’s intentions to use the technology will be greater in spite of their attitude toward the technology alone, if they expect a technology to increase their performance on the job. Many existing studies have shown that perceived usefulness directly and significantly influences behavioural intention to use a particular online system (Chen and Ching, 2002; Chen et al., 2002; Heijden et al., 2003; Guriting and Ndubisi, 2006; Khalifa and Shen, 2008; Liao et

al., 2007; Lin and Wang, 2005; Luarn and Lin, 2005; Wei, et al., 2009; Lai and Yang, 2009). Consequently, we propose the following hypothesis:

H5. Perceived usefulness will have a positive effect on the individual’s intention to use e-procurement system.

Complexity of one particular system will become the inhibitor that discourages the adoption of an innovation (Rogers, 1995). The existing studies suggest that perceived ease of use is a major attribute of e-business applications such as internet commerce (Chen et al., 2002; Heijden et al., 2003), online banking (Guriting and Ndubisi, 2006), and mobile commerce (Lin and Wang, 2005; Luarn and Lin, 2005). Users would be concerned with the effort required to use that application and the complexity of the process involved. Such perceived ease of browsing, identifying information and performing transactions should enable favorable and compelling individual experience (Liao and Cheung, 2001; Chen et al., 2002; Heijden et al., 2003). Thus, this study examines the following hypothesis:

H6. Perceived ease of use will have a positive effect on the individual’s attitude toward e-procurement system.

TAM suggests that perceived ease of use is thought to influence the perceived usefulness of the technology. The easier it is to use a technology, the greater the expected benefits from the technology with regard to performance enhancement. This relationship has also been validated in online technology context (e.g., Gefen and Straub, 2003; Gefen et al., 2003; McCloskey, 2006; McKechnie et al., 2006; Moon and Kim, 2001; Morosan and Jeong, 2008). Based on these arguments, we propose the following hypothesis:

H7. Perceived ease of use will have a positive effect on the perceived usefulness of e-procurement system.

Based on the preceding hypotheses, the research model is developed and illustrated in Figure 1. The model involves 6 constructs, which includes perceived ease of use, subjective norm and perceived behavioural control as independent variables, perceived usefulness and attitude toward e-procurement as intervening variables, and intention to use e-procurement as the dependent variable.

Figure 1. Research model Research method and design

Data collection

Since this paper aimed to examine the effects of TAM and TPB variables on the intention of e-procurement adoption, a self-administered questionnaire was used to target purchasing/supply managers and officers in the United Arab Emirates (UAE) organizations who had not yet used e-procurement. Immediately after the questionnaire distribution, a letter soliciting internal promotion of the study was faxed to all participating organizations. Respondents were asked to complete the questionnaires individually and return them to designated department secretaries, from whom the questionnaires were collected. A total of 500 questionnaires were distributed to randomly selected organizations which were drawn from economic and commercial directories published by chambers of industry and commerce in seven UAE Emirates including Abu Dhabi, Dubai, Sharjah, Ajman, Ras al-

H3

H1

H2

H5

H4

H6

H7

TAM

TPB

Ease of use

Attitude

Usefulness

Subjective Norm

Behavioural Control

Intention to use E-procurement

Khaimah, Fujairah and Umm al-Quwain. A total of 329 completed questionnaires were returned. A total of 13 questionnaires were discarded because of incomplete data, leaving 316 usable questionnaires for this study with a response rate of 63%. The profile of respondents’ demographics is presented in Table I. Table I. Profile of respondents Demographic variables n % Demographic variables n %

Position Number of employees CEO/director/general manager 65 20.6 < 50 85 26.9 Purchasing/supply manager 127 40.2 50 – 199 142 44.9 Purchasing/supply officer 103 32.6 200 – 500 73 23.1 Other positions 21 6.6 > 500 16 5.1 Purchasing experience Industry type < 5 years 53 16.8 Manufacturing 174 55.1 5 – 10 years 79 25 Banking/Finance/Insurance 28 8.9 > 10 years 184 58.2 Healthcare and hospitality 7 2.2 Retail/Trading/Wholesale 61 19.2 Age Travel/Tourism/Hotel 8 2.5 < 25 11 3.5 Education and training 3 1.0 26 – 29 57 18 Computer/IT 12 3.8 30 – 39 129 40.8 Logistics/Communications 17 5.4 40 – 50 84 26.6 Engineering/Construction 6 1.9 > 50 35 11.1 Educational level Undergraduate 93 29.4 Graduate 207 65.5 Post graduate 16 5.1

The majority of respondents were purchasing managers or officers (72.8%) in the age group of 30 – 50 (67.4%), with graduate education (65.5%) and purchasing experience over 10 years (58.2%). Most of the respondents represented the manufacturing sector (55.1%) and small/medium size organizations that have up to 199 employees (71.8%). Measures

A two-part questionnaire was designed. The first part involves nominal scale items used to collect basic information about respondents’ demographics including position, purchasing experience, age, educational level, number of employees, and industry type. The second part includes seven-point Likert scales, ranging from (1) “strongly disagree” to (7) “strongly agree”, used to operationalize the constructs included in the investigated research model; intention to use, attitude, usefulness, ease of use, subjective norm and behavioural control. The questionnaire items were mostly adopted from relevant prior research, with necessary validation and wording

changes tailored to e-procurement system and the targeted professional context as shown in Table II.

Table II. Question items used in the study

Construct and items

Measure Source

Attitude

AT1 Using e-procurement technology is a good idea.

AT2 Using e-procurement technology would be a wise idea.

AT3 Using e-procurement technology in procurement is unpleasant.

AT4 It is desirable to use e-procurement technology.

Wu and Chen (2005), Cheng et al. (2006) and Lai and Li (2005).

Perceived ease of use

EOU1 Learning to use e-procurement technology would not be easy for me.

EOU2 The interaction with e-procurement does not require a lot of mental effort.

EOU3 It is easy to use e-procurement technology to accomplish my procurement tasks.

Cheng et al. (2006) and Lai and Li

(2005).

Perceived usefulness

U1 Using e-procurement technology would enable me to accomplish my tasks more quickly.

U2 Using e-procurement technology would make it easier for me to carry out my tasks.

U3 E-procurement technology is useful.

U4 Overall, using e-procurement technology is advantageous.

Wu and Chen (2005), Cheng et al. (2006) and Lai and Li (2005)

Perceived behaviour control

BC1 I would be able to use e-procurement technology well for procurement tasks.

BC2 Using e-procurement technology would be entirely within my control.

BC3 I have the resources, knowledge, and ability to use e-procurement technology.

Wu and Chen (2005)

Subjective norm

SN1 People who are important to me would think that I should use e-procurement technology.

SN2 People who influence me would think that I should use e-procurement technology.

SN3 People whose opinions are valued to me would prefer that I should use e-procurement technology.

Chau and Hu (2002) and Wu and Chen (2005)

Intention

INT1 I would use e-procurement technology for my procurement needs.

Wu and Chen (2005),

INT2 Using e-procurement technology for handling my procurement tasks is something I would do.

INT3 I would see myself using e-procurement technology for handling my procurement tasks.

Cheng et al. (2006) and Lai and Li (2005)

Data Analysis and findings

Content validity deals with how representative and comprehensive the items are in creating the scale. It is assessed by examining the process by which scale items are generated. Content validity in this study should be relatively acceptable since the various parts of questionnaire were all adapted from the literature. Furthermore, a pre-test is performed to validate the research instrument within the targeted context since its validity may not be persistent across different technologies and user groups (Straub, 1989). As suggested by Cooper and Schindler (2003), a panel of persons can be interviewed to judge how well the instrument meets the standards. Thus, the researcher conducted independent interviews with 7 respondents who had more than 3 years experience using e-procurement technology. Respondents were asked to comment on the length of the instrument, the format, and the wording of the scales. They suggested that the procedure and Arabic translation of the questionnaire were generally appropriate, with some modifications in the translated version of the questionnaire. Respondents who had participated in the pre-test were excluded from the subsequent study.

The measurement model

The goodness-of-fit measures were used to assess the overall model fit. The overall fit indices for the proposed model satisfied the cutoff criteria stated by Ahire et al. (1996), Garver and Mentzer (1999), Hair et al. (1998), Hu and Bentler (1999), and Koufteros (1999). The ratio of Chi-square to the degree of freedom (χ2=df) of 1.97 (p < 0.001), The Comparative Fit Index, CFI = 0:961 > 0:95; the Tucker–Lewis index, TLI = 0:952 > 0:95; the Root Mean Square Error of Approximation, RMSEA = 0.053 < 0:08, Goodness-of-Fit Index (GFI) = 0.92, Adjusted Goodness-of-Fit Index (AGFI) = 0.87, Relative Fit Index (RFI) = 0.95, and Normed Fit Index (NFI) = 0.96, The conformity factor analysis showed that the indices were over their respective common acceptance levels as suggested by prior research, therefore the proposed model generally fits the sample data well. After achieving adequate overall fit indices, the measurement model was further evaluated for its unidimensionality and convergent validity. Reliability was assessed at two levels: item reliability and construct reliability (Hair et al., 1998, Koufteros, 1999). As shown in Table III, the reliabilities of the different measures (R2) included in the model were greater than 0.50, ranged from 0.58 to 0.97, demonstrating that item reliability was satisfied (Koufteros, 1999). Garver and Mentzer (1999) and Hair et al. (1998) recommend computing Cronbach's coefficient alpha to assess scale reliability, with alpha values greater than or equal to 0.70 indicating sufficient reliability. As reported in Table III, alpha scores for all of the measurement scales exceeded the 0.70 cut-off value. Alpha values for attitude, ease of use, usefulness, behaviour control, subjective norm, and intention are 0.95, 0.96, 0.92, 0.95, 0.91 and .98 respectively. The study scales are sufficiently reliable. The Construct Composite Reliability (CCR) for all the constructs were ranging from 0.89 to 0.97 (0.93 for attitude toward use, 0.96 for perceived ease of use, 0.91 for perceived usefulness, 0.94 for perceived behaviour control, 0.89 for subjective norm, 0.97 for intention to use) thus indicating good item reliability that exceeded the 0.7 cut-off value (Hair et

al., 1998). The Average Variance Extracted (AVE) ranged from 0.71 to 0.84 which exceeded the 0.5 threshold value (Hair et al., 1998). In addition, the convergent validity of the scales is

contrasted since all items prove significant (p = 0.001) and standardised lambda coefficients are above 0.50 cut-off value. Discriminant validity was assessed using a chi-square difference test for each pair of scales under consideration, with a statistically significant difference in chi-squares indicating validity (Garver and Mentzer, 1999; Ahire et al., 1996). All possible pairs of the study scales were subjected to chi-square difference tests with each pairing producing a statistically significant difference (p < 0.05). Additionally, discriminant validity is established if the AVE is larger than the squared correlation coefficients (r2) between variables (Fornell and Larcker, 1981). Table IV shows that the square roots of the AVE scores (diagonal elements in italic) were all higher than the correlations among the constructs (off-diagonal elements) in the same row and column. For example, perceived ease of use (EOU) exhibited high discriminant validity from all other constructs. The AVE for EOU was 0.88 while the shared variance between EOU and other constructs ranged from 0. 45 and 0.68, an indication of discriminant validity. Table III.

Measurement model results

Construct/Items

Factor loading

t-value Composite reliability

Average variance extracted (AVE)

Cronbach’s alpha

Attitude 0.93 0.7662 0.95

AT1 0.851 18.784

AT2 0.922 22.582

AT3 0.816 17.261

AT4 0.882 19.633

Perceived ease of use 0.94 0.7744 0.96

EOU1 0.916 21.168

EOU2 0.918 21.354

EOU3 0.865 19.426

Perceived usefulness 0.91 0.7631 0.92

U1 0.866 19.451

U2 0.836 17.739

U3 0.852 18.149

U4 0.874 19.361

Perceived behaviour control 0.94 0.7786 0.95

BC1 0.907 20.610

BC2 0.861 19.167

BC3 0.870 19.583

Subjective norm 0.89 0.7134 0.91

SN1 0.847 17.151

SN2 0.862 19.374

SN3 0.880 19.702

Intention 0.97 0.8357 0.98

INT1 0.917 21.465

INT2 0.912 21.158

INT3 0.902 20.563

Table IV. Discriminant validity

Construct ATT EOU U BC SN INT

Attitude (AT) 0.87

Perceived ease of use (EOU) 0.65 0.88

Perceived usefulness (U) 0.76 0.64 0.82

Perceived behaviour control (BC) 0.66 0.45 0.34 0.88

Subjective norm (SN) 0.65 0.51 0.64 0.38 0.84

Intention (INT) 0.67 0.68 0.75 0.56 0.65 0.91

Note: all correlations are significant at p < 0.05. Diagonal elements are square roots of the AVE

Analysis of the structural model

The structured equation modelling (SEM) is used to assess the causal structure of the proposed model in this study. Accordingly, common model goodness-of-fit measures are considered. The results of structural equation modelling obtained for the proposed model revealed a ratio of Chi-square to the degree of freedom (χ2=df) of 1.98 (p < 0.001), Goodness-of-Fit Index (GFI) = 0.93, and adjusted Goodness-of-Fit Index (AGFI) = 0.89, Root Mean Square Error of Approximation (RMSEA) = 0.06, comparative fit index (CFI) of 0.96, normed fit index (NFI) of 0.97 and relative fit index (RFI) of 0.96. Furthermore, the standardized path coefficients are all significant at 0.001 level except for the paths from subjective norm and behavioural control to intention which are significant at 0.01. As shown in Figure 2, intention to use e-procurement system in this study is jointly predicted by attitude (β = 0.35, p<0.001), perceived usefulness (β = 0.29, p<0.001), subjective norm (β = 0.24, p<0.01), and behavioural control (β = 0.21, p<0.01), rendering support for H1, H2, H3, and H4. These variables totally explain 62% of the variance on intention to use e-procurement system (R2 = 0.62). Moreover, both perceived usefulness (β = 0.41, p<0.001) and perceived ease of use (β = 0.33, p<0.001) have significant effect on the individual’s attitude toward e-procurement system, thus supporting H5 and H6. Together, perceived usefulness and perceived ease of use account for 0.68% of the observed variance attitude (R2 = 0.68). The effect of perceived ease of use on perceived usefulness is positively strong (β = 0.46, p<0.001), validating H7. The explanatory power of perceived ease of use contributes by 31% in explaining the variance in perceived usefulness (R2 = 0.31).

Notes ** P<0.01, *** P<0.001

Figure 2.

The structural model

Discussion

The results of this study provide support for the research model presented in Figure 1 and for the hypotheses regarding the directional linkage among the model’s variables. The overall explanatory power of our research model was relatively high with an R-square of 62 % for intention to use e-procurement. This is relatively high percentage when compared with previous studies in IS acceptance including Taylor and Todd (1995) with R2 = 0.60, Bhattacherjee (2000) with R2 = 0.52, and Chau and Hu (2002) with R2 = 0.42, Chau and Hu (2002) with R2 = 0.43, and Wu and Chen (2005) with R

2 = 0.69. This finding suggests that the extended TAM with TPB model is capable of explaining a relatively high proportion of variation of intention to use e-procurement. Several insightful results could be summarized from our research framework as follows. First, in comparing path coefficients of antecedents of intention to use e-procurement, attitude emerges as the most powerful predictor (β = 0.35, P<0.001) of the intention to use e-procurement relative to the other factors. This singles out the importance of developing and managing user’s attitude to ensure successful implementation of e-procurement systems. Even though this finding is inconsistent with previous research (Chau and Hu, 2002; Davis et al., 1989; Heijden, 2003), it is similar to those of Chapman (2000), Davis (1993) and Wu and Chen (2005). Furthermore, this study indicates the importance of user’s attitude as a determinant of behavioural intention in pre-implementation stage as shown in the case of e-procurement system. This result contradicts Davis et al’s (1989) implication that attitude is not a determinant of behaviour intention in pre-implementation stages. The reason may be related to the level of user awareness of technology as it involves the diffusion of knowledge and information that allow user to discover and think of the new technology, even in the pre-implementation stages. Second, our results suggest that perceived usefulness appears to be the second most important determinant (β = 0.29, P<0.001) of the intention to use e-procurement. This result is similar to the

R2 = 0.62

R2 = 0.31

R2 = 0.68

0.21**

0.35*** 0.24**

0.29*** 0.46***

0.41***

0.33***

Ease of use

Attitude

Usefulness

Subjective Norm

Behavioural Control

Intention to use E-procurement

finding reported in Chan and Lu (2004), Davis (1993), Davis et al. (1989), Pikkarainen (2004), Szajna (1996) and Taylor and Todd (1995), which indicated that perceived usefulness has a significant direct influence on behavioural intention toward system use. This finding reflects the pragmatic dimension in e-procurement adoption decision that a user is likely to accept e-procurement system when it is considered to be useful and fill service needs. Moreover, usefulness has an indirect influence, via attitude (β = 0.41, P<0.001), on behavioural intention to use e-procurement. This result is similar to the finding reported by Taylor and Todd (1995), which indicated that perceived usefulness has both direct and indirect influences on behavioural intentions toward system use. Accordingly, users are likely to form a position attitude toward e-procurement system when it is proven as a useful utility to the practice. In a field study of industrial supply, Mukhopadhyay and Kekre (2002) found that buyer and supplier can significantly derive strategic benefits when either of them initiates an e-procurement system. Third, user’s perceived subjective norm appeared to be the third most important determinant of the intention to use e-procurement (β = 0.24, P<0.01). The result is similar to the finding reported by Bhattacherjee (2000) and Karahanna et al. (1999), but differs from those of Taylor and Todd (1995) and Chau and Hu (2002). However, Venkatesh and Davis (2000), Mathieson (1991) and Davis et al. (1989) gave an interpretation in that subjective norm could significantly determine intention to use in a mandatory-usage context, but its impact would become less significant while users are in a voluntary-usage context. From this perspective, e-procurement may follow the mandatory form when new e-procurement adopters, buyers or suppliers, are likely to develop dependent evaluations and consequently may place more weight on prior e-procurement adopters’ opinions. This is facilitated by the availability of knowledge and references about e-procurement system in use within the business community. To confirm this interpretation, the study of Davila et al. (2003) demonstrates that “follower” firms value the lessons they learn from their more venturesome counterparts who innovate with newer e-procurement technologies. The findings also show encouraging signs of wider adoption of e-procurement as more firms come forward with their pioneering implementation experiences and as more and more firms take internet-enabled supply chain management initiatives more seriously. Fourth, behavioural control reflects people’s perception of ease or difficulty in performing the behaviour of interest (Ajzen, 1991). User’s behavioural control in this study appeared to have significant effect on the intention to use e-procurement systems but not to an extent comparable to attitude. Although Ajzen and Madden (1986) claim that the perceived behavioural control is less likely to be related to intention, this finding is in harmony with Taylor and Todd’s (1995) and Mathieson’s (1991) recognition of perceived behavioural control as an important determinant of behavioural intention. A plausible explanation for the significant effect is that the operations of e-procurement technology in general may be particularly complicated for inactive e-procurement users, as in this study, especially when considering users’ diverse competence and learning capability. Also, Mathieson (1991) supports this view by stating that user perceptions of system control increase when they become more unaware of the system functions, knowledge, and acquirable resources to use the system. In contrast, active users of new internet-based technology perceive it as a natural extension of their online world, so that they do not really view its usage as a challenging activity (Lassar et al., 2005). Therefore, practitioners should give an extra attention to prevent e-procurement users from consequently experiencing access difficulties, system crashes, drop outs, service delays and system malfunctions to create a positive control sense over the procurement system and in turn, increasing its level of adoption.

Fifth, perceived ease of use has emerged in this study as a significant predictor of both attitude (β = 0.33, P<0.001) and usefulness (β = 0.46, P<0.001) which in turn leads to greater acceptance of e-procurement. This finding contradicts prior research that considers perceived ease of use as a basic requirement for system design and should not have an influence on attitude in the later stages of adoption (Adams et al., 1992; Agarwal and Prasad, 1998; Chau and Hu, 2002; Davis et

al., 1989; Karahanna et al., 1999). However, findings of this research were in accordance with the results by Kim et al. (2008), Lee (2009), Moon and Kim (2001), Wu and Chen (2005) and Yu et al. (2005) which showed perceived ease of use had direct effect on perceived usefulness and attitude toward use. Several explanations could be stated for this finding. Since the research data in this study includes a sample of inactive e-procurement users as mentioned earlier, it could be implied that inexperience in e-procurement technology possibly increases user’s effort expectancies of the system. Furthermore, research provides empirical support for this implication that perceived ease of use becomes a significant predictor of attitude and usefulness when users are unfamiliar with the system (Agarwal and Prasad, 1999; Liaw, 2002; Szajna, 1996). Therefore, e-procurement technology professionals should consider increasing the attractiveness of the system by creating e-procurement system that has user-friendly, easy-to-control and informative interfaces, fast websites access and page downloads, and short transaction times.

Conclusions and future research

This study aimed to establish an integrated research model to predict and understand intention to use e-procurement technology. The proposed model in this research incorporated TAM and TPB constructs to provide a more comprehensive investigation covering both technical and social aspects of e-procurement technology. Empirical data was collected from a field survey to verify the fitness of the hypothetical model. The measurement model indicates the theoretical constructs have adequate reliability and validity, while the structured equation model (SEM) was verified to having a high model fit for the empirical data. The research findings show that behavioural intention toward e-procurement technology is mainly determined by user’s attitude and additionally influenced by perceived usefulness and subjective norm. Overall, the results show that the proposed model has good explanatory power and confirms its robustness, with a reasonably strong empirical support, in predicting users’ intentions to use e-procurement technology. This could be especially valuable for vendors, professionals and users of e-procurement technology for system development and implementation purposes. As with any research, care should be taken when generalizing the results of this study. First, selection bias could be a problem because only inactive e-procurement users were used in the data collection process. However, Karahanna et al. (1999) suggested that determinants of behavioural intention change in terms of users’ level of experience. Therefore, future studies should pay extra attention to collecting data from both inexperienced and experienced e-procurement users. This will remedy the bias and help researchers to better understand the e-procurement adoption. Second, findings of this research are based on snapshot cross-sectional survey data that reduce the ability to reflect the changes in the research constructs, particularly when e-procurement experiences increase. Thus, future research may consider qualitative approaches including grounding theory or case study research to gain in-depth understanding of factors that influence of e-procurement adoption in newly investigated contexts. Besides, using a longitudinal study in future research will provide more comparative insights into e-procurement adoption at different time periods. Third, although our model provides some insights to explain the intention of e-procurement adoption, some possible moderating effects are not well

understood. Therefore, future studies should extend the TAM and TPB models by adding important factors toward actual use such as organizational-related factors (e.g., top management support and organisational capability) or user-specific constructs (e.g., innovativeness and expressiveness) to increase the model’s predictive power in the e-procurement context. Finally, findings of this study are based on the presumption that user decision to use e-procurement technology is on voluntary basis. Therefore, future research can target e-procurement adoption in a mandatory environment where users’ attitude plays less important role.

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