Critical factors for cloud based e-invoice service adoption in Taiwan: An empirical study

International Journal of Information Management 35 (2015) 98–109

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International Journal of Information Management journal homepage: www.elsevier.com/locate/ijinfomgt

Critical factors for cloud based e-invoice service adoption in Taiwan: An empirical study Jiunn-Woei Lian ∗ Department of Information Management, National Taichung University of Science and Technology, 129 Sec. 3, San-min Rd., Taichung 40401, Taiwan

a r t i c l e

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Article history: Keywords: Cloud computing e-Government e-Invoice Critical factor Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)

a b s t r a c t Cloud computing is the current trend in online information services, and e-government is no exception. Previous literature on e-government service adoption has focused on traditional web based online services (with the data or software residing on the client and server). However, given the distinguishing characteristics and new business model of cloud computing (data or software in the cloud), more study of cloud based e-government services is warranted. We propose an integrated model which is based on the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). We conduct an empirical study to understand the critical factors for the adoption of cloud based e-invoicing, a novel e-government service in Taiwan. A total of 251 valid responses to our questionnaire survey were received. The results indicate that effort expectation, social influence, trust in e-government, and perceived risk have significant effects on the intention to adopt e-invoicing. Additionally, trust in e-government and perceived risk mediates the relationship between behavioral intention and security concerns regarding e-government. Gender differences moderate the relationship between social influence and behavioral intention. Age level is found to moderate the relationship between perceived risk and behavioral intention. These findings contribute to academic research and have practical implications, advancing our understanding of cloud based e-government applications. © 2014 Elsevier Ltd. All rights reserved.

1. Introduction 1.1. Background Cloud computing technology has become an important milestone in the development of information systems (Lian, Yen, & Wang, 2014). Google, IBM, Amazon, and Microsoft have all made significant investments in developing the cloud computing environment. At present, both businesses and governments are committed to delivering on this new platform in order to improve their online and mobile services (Pokharel & Park, 2009). In its official “Knowledge of Economic Development Programs” (National Development & Council, 2000), the Taiwanese government announced the development of e-invoicing as one of its major e-government policies. The scope of this policy is winder than any other e-government application, because it relates to the daily operations of every business and the daily life of all citizens. In 2011, Taiwan’s e-invoice development was honored with the FutureGov award for Public Sector Organization of the Year, North Asia

∗ Tel.: +886 4 22196600; fax: +886 4 22196311. E-mail address: [email protected] http://dx.doi.org/10.1016/j.ijinfomgt.2014.10.005 0268-4012/© 2014 Elsevier Ltd. All rights reserved.

(Futuregovt, 2011). Therefore, e-invoice development in Taiwan is a good, representative example of a cloud based e-government application. E-invoice is originated in Scandinavia. Standardized invoices are issued, transmitted, and received electronically via the Internet. They can be transmitted between business operators, saving costs and enhancing operational efficiency. E-invoice systems allow organizations to go paperless, protecting the environment and increasing efficiency. Countries around the world, including the U.S, Finland, Denmark, Sweden, and Belgium, have committed to promoting this e-government application. Taiwan is one of the major pioneers in the Asia-Pacific region. E-invoicing has many benefits but obstacles include security concerns and the potential for fraud (The European Commission’s Directorate-General for Enterprise and Industry, 2014). On November 29, 2000, Taiwan’s Ministry of Finance (part of the Executive branch, under the President) announced the operational guidelines for a test system for electronically transmitted standardized invoices. The test system began operation on December 1, 2000. On December 18, 2010, physical consumption channels began to issue e-invoices. Going paperless is accomplished in three stages. In the first stage, the adjustment period, people who do not yet use carriers (e.g., credit cards, ATM cards or cellphones) to

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E-invoice service cloud platform of the Ministry of

E-invoice platform setup

99

Transactions invoice data

Finance Citizens

Businesses Transactions

Fig. 1. E-invoice system architecture in Taiwan.

receive e-invoices electronically are provided with paper printouts of their e-invoices. In the second stage, the growth period, people are invited to use a variety of carriers to receive e-invoices. This is to enhance convenience and reduce the demand for printed e-invoices. In the third stage, the maturation period, people are already accustomed to e-invoices, and business operators no longer need to provide printouts. The Taiwanese e-invoice system operates on the service platform of the Ministry of Finance (https://www.einvoice.nat.gov.tw). The architecture is illustrated in Fig. 1. The cloud-based system provides a portal site for consumers, business operators, government agencies, external agencies, and social welfare organizations. By providing uniform transmission standards and integrated service to users (both consumers and enterprises), people can manage their e-invoices via the Citizen Digital Certificate to ensure security. Business operators can store receipts and discount coupons in the e-invoice platform and subsequently send them to other relevant customers. During data transmission, digital signatures and encryption techniques are employed to ensure confidentiality and security. 1.2. Motivations and purposes Previous literature on e-government service adoption has focused on traditional online web services, in which the data or software are located on the client and server devices (Al-adawi, Yousafzai, & Pallister, 2005; AlAwadhi & Morris, 2008; Carter & Belanger, 2005; Hung, Chang, & Yu, 2006). However, given the distinguishing characteristics of cloud computing (in which the data or software reside in the cloud), further study is needed to show whether previous findings apply to this new paradigm. Therefore, research on cloud based e-government service adoption is required. This is the major motivation for our study. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) (Venkatesh, Thong, & Xu, 2012) indicated that variables specific to each different context must be included when attempting to understand user acceptance of information technology (IT) applications. The unique characteristics of cloud computing and e-government services suggest the need for a study investigating user acceptance of cloud based e-government services. Thus, the second motivation for our study is the need to integrate the UTAUT2 with context variables such as security concerns, trust, and perceived risk. Over the past 10 years, Taiwan has developed a world class egovernment service. Brown University has reported that Taiwan’s e-government service ranks consistently among the top three, worldwide (West, 2007). The 2013 Waseda University International E-government Ranking placed Taiwan 8th in the world (Obi, 2013). Switzerland’s International Institute for Management Development ranked Taiwan’s government 8th in efficiency and 5th in technology infrastructure (World Competitiveness Online, 2013). The Digital Opportunity Index (DOI) of the International Telecommunication Union (ITU) ranked Taiwan number 7 among 181 economies (ITU, 2007). The Taiwanese government has made the development of e-government a critical goal for the

country’s future. Cloud based e-government services are at the core of that development. One major objective is the implementation of a paperless, cloud based e-invoice. Understanding the experiences, lessons, issues and challenges encountered by the Taiwanese during this implementation can benefit e-government professionals worldwide, especially those in developing countries aspiring to improve e-government services, and particularly those in the AsiaPacific region. For this reason, we conducted an empirical study identifying the critical factors that affect the adoption of government e-invoices in Taiwan. Finally, this study is based on previous research on information system adoption (UTAUT2) and integrates context variables relate to cloud computing (perceived risk, trust in e-government, and security concerns regarding e-government). By examining these critical factors in the new context (cloud based e-invoice service), we can increase their generalizability. Thus, the purpose of this study is to understand the critical factors of cloud based e-invoice services in Taiwan. First, we identify the factors which are critical to the adoption of cloud based egovernment. Second, we determine whether there is a difference between the adoption of cloud based e-government and previous adoptions of traditional e-government services. Finally, for academic and practical reference, we summarize the Taiwanese experience. 2. Literature and theoretical background 2.1. Cloud computing The National Institute of Standards and Technology (NIST) defined cloud computing as “a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications, and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction” (Mell & Grance, 2011). Advantages include on-demand self-service, broad network access, resource pooling, rapid elasticity, and measured service for business (Mell & Grance, 2011). Armbrust et al. (2010) defined cloud computing as “both the applications delivered as services over the Internet and the hardware and systems software in the data centers that provide those services.” Cloud computing has three service models: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). The deployment models are private cloud, community cloud, public cloud and hybrid cloud (Mell & Grance, 2011). Security issues become critical during service delivery (Subashini & Kavitha, 2011). 2.2. Cloud based e-government services Cloud computing is a new channel for the delivery of government services (Smitha, Thomas, & Chitharanjan, 2012). It can help to improve government performance and create novel services (Hashemi, 2013; Liang, Liang, & Wen, 2011). In the U.S., government has begun to deliver services on this platform to improve

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Table 1 Critical factors for e-government adoption. Articles

Wang (2014) Shin (2013) Hung et al. (2013) Hernandez-Ortega (2012) Ozkan and Kanat (2011) Smith (2010) Chan et al. (2010) Lean et al. (2009) Srivastava and Teo (2009) AlAwadhi and Morris (2008) Belanger and Carter (2008) Carter and Weerakkody (2008) Horst et al. (2007) Hung et al. (2006) Al-adawi et al. (2005) Carter and Belanger (2005) Wu and Chen (2005) Gilbert et al. (2004)

Critical factors Effort expectation/Perceived ease of use

Performance expectation/Perceived usefulness √

Social influence

Facilitating conditions

Risk

Trust

√ √ √

√ √ √

√

√

√

√ √

√ √

√

√

√ √ √ √ √

√

√

√

√

Security

√ √

√

√

√ √

√ √

√

e-government service quality (Paquette, Jaeger, & Wilson, 2010; Pokharel & Park, 2009). Using cloud computing to deliver government services provides benefits such as overall reduced cost, distributed data storage, scalability, accountability, modifiability and security management (Smitha et al., 2012). Although cloud computing can benefit e-government services, there are risks, both tangible (access, availability, infrastructure, and integrity) and intangible (reliability of the cloud, security, safety mechanisms, data confidentiality and privacy, and so on). Security related issues are major sources of risk (Zissis & Lekkas, 2011). Therefore, a defined risk management plan is required for cloud based e-government (Paquette, Jaeger, & Wilson, 2010). 2.3. E-invoice The e-invoice is a kind of information system service that gathers transaction information and transmits it through a network (Hernandez-Ortega, 2011). In this era of e-business, it plays a critical role in maintaining business information throughout the supply chain (Chang et al., 2013). Electronic data interchange (EDI) was initially implemented between businesses only. Later, the Internet was employed to transmit e-invoices between individuals, businesses, and government, becoming the backbone for e-commerce. Now, many countries have employed cloud computing to provide e-invoice services for all stakeholders. Compared with traditional paper invoices, the e-invoice can help businesses achieve paperless, transparent transactions. Many previous studies have paid attention to e-invoice adoption. However, most of them are focused on the business/firm level (Hernandez-Ortega, 2011; Vrcek & Magdalenic, 2011). Little attention has been paid to the individual, creating a research gap in understanding individual user acceptance of e-invoicing. Closing that gap is one of the purposes of this study. 2.4. Critical factors for e-government adoption Previous studies on e-government adoption issues have been based on information management related theories. The technology acceptance model (TAM) proposed by Davis (1989) provides the primary theoretical background for many of these studies (e.g., Al-adawi et al., 2005; Carter & Belanger, 2005; Wang, 2014). The theory of planned behavior (TPB) has also been employed to understand online tax filing/payment systems and mobile e-government

√

√

√ √ √

√ √ √ √ √ √ √ √

√

services (e.g., Hung et al., 2006; Hung, Chang, & Kuo, 2013). Wu and Chen (2005) integrated TAM and TPB, proposing an integrated model by which to understand online tax acceptance. Venkatesh, Morris, Davis, and Davis (2003) proposed the UTAUT model to explain users’ behavioral intention toward information systems. UTAUT has become the major theoretical basis for research on egovernment adoption (e.g., AlAwadhi & Morris, 2008). In addition to the user acceptance view, the perspective of trust is another major theoretical structure employed to understand user acceptance of e-government services. Table 1 shows that most e-government adoption literature discusses trust related issues (e.g., Belanger & Carter, 2008; Carter & Weerakkody, 2008; Gilbert, Balestrini, & Littleboy, 2004; Srivastava & Teo, 2009; Smith, 2010). Trust plays a critical role, and many studies have indicated that security, trust, and risk are all related (Al-adawi et al., 2005; Belanger & Carter, 2008; Horst, Kuttschreuter, & Gutteling, 2007; Hung et al., 2006; Gilbert et al., 2004). Shin (2013) also argued that availability, access, security and reliability are critical features that influence the extent to which users adopt cloud based e-government services. Based on above review, this study proposes seven critical factors for e-government adoption. Note that none of the studies summarized in Table 1 has integrated all of these seven factors. This research gap is one of the motivations for the present study.

2.5. UTAUT and UTAUT2 IT user acceptance research covers one of the most important issues in the information system (IS) management field. Many competing models have been proposed to understand various IT acceptance behavior: the TAM, the theory of reasoned action (TRA), the TPB, and so on (Venkatesh et al., 2003). A comprehensive model was needed to aid in understanding user acceptance of IS/IT. Venkatesh et al. (2003) proposed the UTAUT model to integrate the findings of previous studies on this issue. The UTAUT model includes four critical antecedents (performance expectance, effort expectancy, social influence, and facilitating conditions) which affect both behavioral intention and actual behavior. Gender, age, experience, and voluntariness have been found to moderate the above relationships (Venkatesh et al., 2003). These relationships were also confirmed by Weerakkody, El-Haddadeh, Al-Sobhi, Shareef, and Dwivedi (2013) in the context of e-government.

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Therefore, the UTAUT model is suitable for understanding the adoption of cloud based e-government services. Venkatesh et al. (2012) extended UTAUT and proposed the UTAUA2 model in a consumer context. The major difference between UTAUT and UTAUT2 is the contest variables. UTAUT2 adds three context-specific variables (hedonic motivation, price value, and habit) in order to apply the UTAUT in a consumer context (e.g., consumer usage of mobile Internet services). Since this clarifies the importance of variables specific to the research context, we propose our own context variables for this study.

of IT/IS (Venkatesh et al., 2003, 2012). Lean, Zailani, Ramayah, and Fernando (2009) found that perceived usefulness will influence users’ intentions regarding e-government. In this study, we consider cloud based e-government service as a new kind of IT application, so this variable can be extended to help understand this phenomenon. Citizens who perceive the usefulness of the e-invoice system will be likely to adopt it. Weerakkody et al. (2013) confirmed the critical role of performance expectation in e-government adoption. Park and Ryoo (2013) indicated that expected benefits affect users’ intention to use cloud computing. Therefore, based on the above we make the following hypothesis.

3. Research model and hypotheses

H1. Performance expectation will positively affect users’ intentions to use cloud based e-invoice services.

3.1. Research model Our study employs UTAUT2 as the theoretical basis for the research model. The research context of this study is cloud based e-invoice system adoption behavior. Since the e-invoice system is not for entertainment purposes, hedonic motivation is not a critical antecedent in this context. Using the cloud based e-invoice system is free in Taiwan, so price value is not a critical concern, and since the cloud based e-invoice system is relatively new e-government application in Taiwan, no citizens are habituated to using it. For these reasons, we needed to revise the context related variables. Thus, we proposed an integrated research model. Earlier studies found that security is the major concern for users adopting cloud computing (Armbrust et al., 2010; Jansen & Grance, 2011; Subashini & Kavitha, 2011; Zissis & Lekkas, 2011). The European Commission’s Directorate-General for Enterprise and Industry (2014) has also emphasized that although e-invoicing has many benefits, the main obstacles are security concerns and the potential for fraud. Previous studies have indicated that IS security concerns relate to trust and perceived risk (Fang et al., 2005–2006; Nicolaou & McKnight, 2006; Pavlou & Gefen, 2004). E-government services are no exception (Belanger & Carter, 2008). Therefore, perceived risk, trust in e-government, and security concerns regarding e-government are employed as context variables in our proposed model. The research model and related hypotheses are illustrated in Fig. 2. 3.2. Research hypotheses In the field of information management, performance expectation has served as one of the critical factors for user adoption

Performance Expectation H1 Effort Expectation

Behavioral

H2

Intention Social Influence

Facilitating Conditions

H10 H3

H8

H9

Perceived risk H5

H6

H4 Trust in e-government H7

Moderators: gender (H11) and age (H12) Fig. 2. Research model.

Security concerns regarding e-government

Effort expectation has also served as a critical factor in the UTAUT (Venkatesh et al., 2003) and UTAUT2 (Venkatesh et al., 2012). Weerakkody et al. (2013) confirmed its critical role in the context of e-government services. Lean et al. (2009) found that perceived complexity has a significant effect on users’ intentions regarding e-government services. Park and Ryoo (2013) indicated that expected switching cost will negatively affect users’ intention to use cloud computing. Therefore, this study hypothesizes that an e-invoicing system which is easy to use will encourage citizens to use it. Therefore, we make following hypothesis. H2. Effort expectation will positively affect users’ intentions to use cloud based e-invoice services. The UTATU and UTAUT2 models emphasized the important role of social influence on IT adoption (Venkatesh et al., 2003, 2012). Social influence will also affect user adoption of e-government (Alryalat, Dwivedi, & Williams, 2012). Hung et al. (2006) also emphasized the importance of social influence in the acceptance of e-government services. Park and Ryoo (2013) found that social influence will also positively affect users’ intention to use cloud computing. In this study, we infer that as more and more citizens use e-invoicing, the effect of peer influence will increase. Therefore, we make the following hypothesis. H3. Social influence will positively affect users’ intentions to use cloud based e-invoice services. Although, the UTAUT model indicates that facilitating conditions directly affect user behavior rather than behavioral intention (Venkatesh et al., 2003), subsequent empirical studies found that facilitating conditions also directly affect user behavioral intention (Escobar-Rodríguez & Carvajal-Trujillo, 2014; Martin & Herrero, 2012; Zhou, Lu, & Wang, 2010). AlAwadhi and Morris (2008) also confirmed the critical effect of facilitating conditions on the intention to adopt e-government services. Therefore, we infer that facilitating conditions will affect users’ behavioral intentions regarding cloud based e-government. Since, cloud based e-government service is innovative and new, many citizens may not be familiar with it. Therefore, the better the facilitating conditions, the more likely people will be to use the service. Hence, this study proposes the following hypothesis. H4. Facilitating conditions will positively affect users’ intentions to use cloud based e-government services. Previous studies have found that trust and security concerns directly affect perceived risk (Chang & Chen, 2008; Kim, Ferrin, & Rao, 2008; Warkentin, Gefen, Pavlou, & Rose, 2002). Warkentin et al. (2002) found that citizens’ trust in e-government negatively affects their perceptions of the risk and, thus, affects their intention to engage in e-government. Belanger and Carter (2008) had similar findings. Kim, Ferrin, and Rao (2008, 2009) indicated that, in the context of electronic commerce, trust will affect perceived risk and perceived risk will affect the user’s intention to shop

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online. Pavlou (2003), Chang and Chen (2008) had similar findings regarding the above relationships. In this study, trust is defined as trust in e-government (Belanger & Carter, 2008; Warkentin et al., 2002). Based on the above discussion, the present study proposes trust as one of the critical factors that will affect the perceived risk of using cloud based e-government services. H5. Trust in e-government will negatively affect perceived risk regarding the use of cloud based e-invoice services. Shin (2013) also argued that, in addition to trust, security concerns are one of the critical features that influence the extent to which users adopt cloud based e-government services. Similar findings have also been noted by Warkentin et al. (2002); Kim et al. (2008) and Chang and Chen (2008). Therefore, security concerns regarding e-government will affect the perceived risk regarding cloud based e-government services. The following hypothesis is inferred. H6. Security concerns regarding e-government will positively affect perceived risk regarding the use of cloud based e-invoice services. When users have a higher security concerns regarding information systems, they will have a lower degree of trust toward those systems. Similarly, when users have security concerns they will have a lower lever of trust in electronic commerce transactions (Chellappa & Pavlou, 2002). Kim et al. (2010) found that security concerns affect users’ use of e-payment systems. Perceived trust also serves as a mediator between perceived security and system use. Therefore, we propose the following hypothesis. H7. Security concerns regarding e-government will negatively affect trust in e-government regarding the use of cloud based einvoice services. Regarding the relationship between trust in e-government and the intention to use cloud based e-invoicing. Carter and Belanger (2005) indicated that trust of the Internet and trust of the government will affect users’ intention to use e-government services. People today are familiar with the Internet, so trust of the Internet is not a critical factor in present study. Lean et al. (2009) also confirmed the relationship between trust and the intention to use e-government services. Finally, Kim et al. (2010) found that perceived trust will affect user behavior regarding the use of epayment systems. Based on the above discussion, the following hypothesis is inferred. H8. Trust in e-government will positively affect users’ intentions to use cloud based e-invoice services. Earlier studies found that security is the major concern for users adopting cloud computing (Armbrust et al., 2010; Jansen & Grance, 2011; Subashini & Kavitha, 2011; Zissis & Lekkas, 2011). Fang et al. (2005–2006) also indicated that perceived security will affect the intention to use handheld devices. In the context of e-invoice adoption, Hernandez-Ortega (2012) found that perceived security is one of the critical factors affecting user intention. Therefore, the following hypothesis is inferred. H9. Security concerns regarding e-government will negatively affect users’ intentions to use cloud based e-invoice services. In addition to being based on the user acceptance perspective, this study integrates the characteristics of cloud computing in the context of e-government. Previous studies have indicated that security and risk are major issues in the delivery of cloud based services (Subashini & Kavitha, 2011). E-government service is no exception, as noted by Paquette, Jaeger, and Wilson (2010). Zissis and Lekkas (2011) also emphasized the importance of securing e-government services which have an open, cloud computing

architecture. Therefore, perceived risk serves as one of the critical antecedents of user behavioral intention toward cloud based e-government services (Brender & Markov, 2013). This study also infers that when people have higher levels of security concerns regarding a cloud based e-government service, the perception of the risk will be higher, negatively impacting their intention to adopt the service. Therefore, this paper proposes the following hypothesis. H10. Perceived risk will negatively affect users’ intentions to use cloud based e-invoice services. Prior studies have clearly confirmed the role of gender differences in the acceptance of IT (Akman, Yazici, Mishra, & Arifoglu, 2005; Dwivedi, Papazafeiropoulou, Gharavi, & Khoumbati, 2006; Garbarino & Strahilevitz, 2004; Lian & Yen, 2014; Riedl, Hubert, & Kenning, 2010; Taipale, 2013; Van Slyke, Comunale, & Belanger, 2002; Wu, 2003). UTAUT and UTAUT2 also emphasized the moderating effect of gender differences. Following the UTAUT model, Martins, Oliverira, and Popovic (2014) confirmed the moderating role of gender differences in the context of Internet banking adoption. Akman et al. (2005) indicated that gender differences affect individuals’ usage of e-government. Dwivedi et al. (2006) also found that gender is one of the significant variables distinguishing UK e-government adopters and non-adopters. Taipale (2013) had similar findings, and emphasized gender differences in the use of e-government services. Therefore, we infer the following hypothesis. H11. Gender differences will moderate the relationships between antecedent variables and users’ intentions to use cloud based einvoice services. UTAUT and UTAUT2 indicated that age moderates the relationships between antecedents and behavioral intention. Based on the TPB, Morris and Venkatesh (2000) found that age moderates the relationships between antecedents and information system use in a workplace context. Using the UTAUT model, Lian and Yen (2014) also confirmed the moderating role of age differences in online shopping adoption. Martins, Oliverira, and Popovic (2014) emphasized the moderating role of different age levels in regards to the adoption of Internet banking. Therefore, this study infers the following hypothesis. Age level will moderate the relationships between H12. antecedent variables and users’ intentions to use cloud based einvoice services. 4. Research methodology 4.1. Participants and data collection This study employed the online questionnaire survey approach with the convenience sampling method. The questionnaire was posted on a consumer-related online community to invite users to participate. Participation in the survey was entirely voluntarily. People who understood the e-invoice were qualified respondents. Since the development of e-invoicing in Taiwan is in the initial phases (from the adjustment period to the growth period), the respondents were potential future adopters. In order to ensure that respondents knew the survey was regarding paperless e-invoices in Taiwan, official illustrations of the Taiwanese e-invoice were provided on the first page of the online questionnaire. After confirming that they understood, respondents continued on to subsequent sections of the questionnaire. Partial least squares (PLS) is suitable for sample sizes over 80, or when the sample is larger than ten times the number of independent variables (Barclay, Thompson, & Higgins, 1995). This study has seven independent variables (e.g., the

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Table 2 Measurements. Variables

Operational definition

References

Item numbers

Performance expectation (PE)

“The degree to which citizen believes that using e-invoice will help him or her to attain gains in daily life.”

6

Effort expectation (EE)

“The degree of ease of use with the use of e-invoice.”

Social influence (SI)

“The degree to which an individual perceived that important others believe he or she should use the e-invoice.” “The degree to which an individual believes that an e-governmental and technical infrastructure exists to support the use of e-invoice.” “The degree to which people trusts government e-invoice policy and service.” “The degree to which people concern about the e-government security.”

Davis (1989); Venkatesh et al. (2003); Walczuch et al. (2007) Davis (1989); Venkatesh et al. (2003); Walczuch et al. (2007) Venkatesh et al. (2003)

Venkatesh et al. (2003)

4

Pavlou and Gefen (2004) Parasuraman (2000)

4

Belanger and Carter (2008) Hung et al. (2006, 2013)

2

Facilitating conditions (FC) Trust in e-government (Trust) Security concerns regarding e-government (Sec) Perceived risk (PR) Behavioral intention (BI)

“The degree to which people perceives risk when using e-invoice.” “The degree to which people intents to use e-invoice.”

minimum required sample is 70). Therefore, in order to ensure sample availability, the minimum sample size required for this study was over 80. Data collection was conducted from October to November 2013. In the end, 251 valid questionnaires were received. To avoid “nonresponse bias” in the data, based on the suggestion of Armstrong and Overton (1977) and Hung et al. (2013), the data were randomly divided into two groups to see if there were differences in age and degree of education. The results indicated no significant differences (p > 0.05) between these two groups. In other words, the sample was properly representative for analysis.

4.2. Measurements This study included 8 major variables with a total of 37 items (see Appendix A). Measurements were modified or extended from prior studies and adapted to fit the research context of our study. Table 2 lists the operational definitions, item numbers, and associated references for each variable. Each statement was translated and reviewed by professional language and information management experts. Some original measurements used 7-point Likert scales and others used 5-point scales. However, the scales should to be consistent. Dawes (2008) found that there are no difference between 5-point and 7-point data sets. Revilla, Saris, and Krosnick (2013) also suggested that using a 5-point scale is better than a 7- or 11-point scale. Based on the above suggestions, and to increase the degree of questionnaire parsimony, each variable was measured using a 5-point Likert scale. A higher value meant that the user perceived the statement as being more strongly true. Therefore, the relationships between trust in e-government, perceived risk (H5), security concerns regarding e-government (H7) and behavioral intention (H9 and H10) were negative. The research questionnaire had three parts, the first of which presented the official illustrations of the Taiwanese e-invoice. All respondents had to view these illustrations and confirm they were qualified to answer the subsequent questions. The second part presented the demographic questions. The third section presented the major items for measuring the variables. The questionnaire was verified by three MIS experts to ensure its appropriateness. Next, 10 students were invited to participate in a pretest to revise the wordings and online format. The questionnaire was then modified based on their suggestions. Appendix A lists the items employed in this study.

6

3

9

3

5. Data analysis 5.1. Demographics and descriptive statistics The 251 usable subjects included 113 males (45%) and 138 females (55%). Their ages ranged mainly between 19 and 30 years old (70.9%), consistent with the largest majority of online users in Taiwan. Most subjects had an undergraduate degree (60.2%), which is similar to the general distribution of online users (Table 3). Therefore, the sample in this study is representative. Table 4 shows the descriptive statistics of each variable, grouped by gender. The lowest score for the superset of all samples was for trust, which is lower than 3. Trust ranks particularly low for the female sample. This means that improving citizens’ trust in e-government is still a critical problem, for females in particular. Other factors which showed significant gender differences were performance expectation, effort expectation, social influence, facilitating conditions, security concerns regarding e-government, and behavioral intention toward e-invoicing (Table 4). 5.2. Validity and reliability The validity and reliability of the measurements employed in this study are acceptable for future analysis. Testing results are illustrated in the following part of this section. The acceptable threshold for composite reliability (CR) is >0.7 (Hair, Black, Babin, & Anderson, 2010). Average variance extracted (AVE) should be >0.5 (Fornell & Larcker, 1981). The acceptable Cronbach’s alpha value is >0.7 (Hair et al., 2010). Based on the above criteria, all of the indexes Table 3 Demographics. Variable Gender Male Female Age <18 years old 19–30 years old 31–40 years old 41–50 years old Over 50 years old Education Senior high school and under Undergraduate Graduate and higher

Count (%) 113 (45%) 138 (55%) 3 (1.2%) 178 (70.9%) 37 (14.8%) 27 (10.8%) 6 (2.4%) 16 (6.4%) 151 (60.2%) 84 (33.5%)

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Table 4 Descriptive statistics.

All M F

PE***

EE***

SI***

FC**

Trust

Sec*

PR

BI***

3.68 (0.93) 3.91 (0.94) 3.50 (0.90)

3.74 (0.87) 3.96 (0.86) 3.56 (0.84)

3.15 (1.01) 3.43 (1.01) 2.93 (0.96)

3.27 (0.92) 3.45 (0.91) 3.12 (0.91)

2.97 (0.93) 3.08 (1.01) 2.88 (0.86)

3.61 (0.77) 3.49 (0.86) 3.71 (0.69)

3.49 (0.94) 3.45 (1.02) 3.53 (0.88)

3.61 (1.11) 3.96 (0.97) 3.33 (1.15)

All: all samples; M: male; F: female. There exist significant difference between genders (*p < 0.05; **p < 0.01; ***p < 0.001).

Table 5 Validity and reliability. Variables

CR

AVE

Performance expectation Effort expectation Social influence Facilitating conditions Trust in e-government Security concerns regarding e-government Perceived risk Behavioral intention

0.96 0.95 0.91 0.88 0.92 0.89 0.94 0.98

0.80 0.77 0.77 0.65 0.73 0.52 0.88 0.95

in this study are acceptable (see Table 5). However, one of the nine items measuring security concerns regarding e-government was deleted because of low factor loading (<0.5) (Hair et al., 2010). Therefore, we used eight items to measure this variable in the subsequent analysis. Other measurements remained unchanged from the original instruments. For discriminant validity, the cross loadings should be lower than the loadings of each indicator (Hair et al., 2010). This was also analyzed and we found that no indicators had loadings with lower values than their cross loadings (Table 6). Table 6 shows the cross loadings of each item. Discriminant validity was assessed by examining whether the square root of AVE for each construct was higher than the squared correlation between that construct and all other constructs (Chin, 1998). Table 7 shows the discriminant validity among the employed constructs. Since the diagonal values are larger than other related values, the constructs show acceptable discriminant validity (Table 7).

5.3. Analysis results SmartPLS (Ringle, Wende, & Will, 2005) was employed to verify the research hypotheses. Since educational level is not the main variable in this study, it served as the control variable in the data analysis (Teo, 2001). The analysis results are illustrated in Table 8. The proposed model explains 66% of the variance of users’ behavioral intention toward adopting cloud based e-invoice services (see Table 5). The path coefficients (beta) of H5, H7, H9, and H10 are negative, as hypothesized. The higher the level of perceived trust in e-government, the lower the perceived risk regarding the use of the cloud based e-invoice system. The higher the security concerns regarding e-government, the lower the degree of trust in e-government and the lower the level of intention to use the einvoice system. Similarly, the higher the level of perceived risk, the lower the level of intention to use the e-invoice system. Based on the t-values, the significant variables affecting citizens’ intentions to use the e-invoice system are effort expectation, social influence, trust in e-government, and perceived risk. In other words, H2, H3, H8, and H10 are supported (see Table 8). This study also found that security concerns regarding e-government do not affect behavioral intention to use the e-invoice system directly (H9 is not supported), but the perceived risk and trust in e-government serve as mediators between security concerns regarding e-government and behavioral intention (H6 and H7 are supported). The results indicate that gender differences moderate the relationship between social influence and behavioral intention toward

Factor loading 0.86–0.93 0.81–0.93 0.75–0.94 0.72–0.89 0.83–0.88 0.54–0.86 0.94–0.94 0.97–0.97

R2

Cronbach’s ˛

N/A N/A N/A N/A 0.09 N/A 0.16 0.66

0.95 0.94 0.85 0.82 0.88 0.87 0.87 0.97

the e-invoice system. Age differences also moderate the relationship between perceived risk and behavioral intention toward the e-invoice system. Therefore, H11 and H12 are partially supported in this research. The results of hypothesis testing are summarized in Table 9. 6. Discussions This study proposed an integrated model and supported it using empirical data in the context of cloud based e-invoice implementation in Taiwan. Fig. 3 summarizes the PLS structural analysis results. In line with the results found in the extant literature listed in Table 1, our results also show that effort expectation, social influence, trust in e-government, and perceived risk serve as critical factors for the adoption of cloud based e-invoicing. However, the factors of performance expectation, facilitating conditions, and security concerns were found to be less critical. These findings and comparisons are detailed in the paragraphs below. Regarding the UTAUT2 related variables, this study found that effort expectation and social influence significantly affect users’ behavioral intentions toward cloud based e-invoice services (supporting H2 and H3). Performance expectation and facilitating Performance Expectation 0.10 Effort Expectation

Behavioral

0.32**

Social Influence

Facilitating Conditions

Intention (0.19)**

0.27**

0.06

0.19*

Perceived risk

(0.02)

(0.14) Trust in e-government (0.30)***

: Significant : Non-significant * p<0.05; ** p<0.01; *** p<0.001 Fig. 3. PLS structural results.

0.33*** Security concerns regarding e-government

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Table 6 Cross loadings. Construct

F1

F2

Performance expectation 0.89 0.61 PE1 0.90 0.62 PE2 PE3 0.89 0.59 0.93 0.61 PE4 PE5 0.91 0.64 0.86 0.68 PE6 Effort expectation 0.59 0.84 EE1 EE2 0.60 0.88 EE3 0.56 0.90 0.68 0.81 EE4 0.61 0.91 EE5 0.65 0.93 EE6 Social influence 0.56 0.64 SI1 0.54 0.59 SI2 0.37 0.44 SI3 Facilitating conditions 0.34 0.46 FC1 FC2 0.44 0.60 0.45 0.57 FC3 0.38 0.54 FC4 Trust in e-government 0.42 0.45 Trust1 Trust2 0.58 0.48 0.44 0.49 Trust3 Trust4 0.46 0.42 Security concerns regarding e-government Sec1 (0.11) (0.21) Sec2 (0.15) (0.26) Sec3 0.00 (0.08) Sec4 (0.19) (0.23) Sec5 (0.15) (0.16) Sec6 0.03 0.01 (0.05) (0.14) Sec7 (0.15) (0.12) Sec8 Perceived risk PR1 (0.08) (0.18) PR2 0.01 (0.04) Behavioral intention BI1 0.61 0.66 BI2 0.58 0.64 BI3 0.60 0.65

F3

F4

F5

F6

F7

F8

0.57 0.54 0.45 0.46 0.51 0.52

0.44 0.43 0.45 0.43 0.39 0.54

0.48 0.52 0.46 0.48 0.49 0.56

(0.13) (0.14) (0.11) (0.14) (0.12) (0.18)

(0.09) (0.06) (0.02) (0.01) 0.03 (0.03)

0.57 0.59 0.47 0.49 0.50 0.62

0.49 0.57 0.53 0.54 0.60 0.62

0.56 0.62 0.60 0.55 0.58 0.63

0.46 0.45 0.44 0.49 0.48 0.54

(0.21) (0.24) (0.21) (0.16) (0.20) (0.24)

(0.09) (0.05) (0.11) (0.10) (0.13) (0.13)

0.51 0.55 0.55 0.62 0.59 0.66

0.93 0.94 0.75

0.45 0.46 0.40

0.50 0.48 0.35

(0.19) (0.16) (0.11)

(0.17) (0.11) (0.04)

0.61 0.58 0.36

0.29 0.39 0.46 0.47

0.79 0.89 0.81 0.72

0.36 0.39 0.39 0.42

(0.29) (0.28) (0.15) (0.17)

(0.17) (0.19) (0.01) (0.10)

0.45 0.52 0.43 0.41

0.41 0.40 0.48 0.45

0.38 0.45 0.39 0.42

0.84 0.83 0.88 0.87

(0.33) (0.20) (0.27) (0.21)

(0.29) (0.15) (0.20) (0.17)

0.45 0.55 0.49 0.43

(0.18) (0.25) (0.08) (0.21) (0.07) 0.01 (0.05) 0.02

(0.28) (0.29) (0.13) (0.28) (0.20) (0.04) (0.14) (0.08)

(0.26) (0.33) (0.20) (0.20) (0.20) (0.14) (0.14) (0.15)

0.85 0.86 0.74 0.78 0.69 0.55 0.66 0.54

0.38 0.34 0.18 0.33 0.29 0.07 0.20 0.10

(0.22) (0.26) (0.08) (0.27) (0.28) (0.01) (0.23) (0.13)

(0.15) (0.09)

(0.19) (0.09)

(0.25) (0.19)

0.31 0.39

0.94 0.94

(0.27) (0.17)

0.61 0.57 0.59

0.56 0.55 0.53

0.56 0.55 0.53

(0.28) (0.28) (0.28)

(0.22) (0.24) (0.22)

0.97 0.97 0.97

conditions are not significant factors (thus, H1 and H4 were not supported). This study infers that Taiwan’s e-invoicing is still in a transition period, so e-invoices and paper invoices are used in parallel. Citizens can choose either one, as desired. Thus, for users, performance expectation is not such a critical factor. Facilitating conditions were also found to be insignificant in the present study, because mobile devices are the major platform for citizens using cloud based e-invoice services. These devices are far more user friendly than are traditional devices. Since users already find their chosen platform easy to use facilitating conditions are not a significant issue in the context of e-invoices. We suggest that future researchers repeat this study when pure paperless e-invoices are fully implemented, between the various development periods.

As for the moderating effects of gender differences, this study found that gender differences do moderate the relationship between social influence and behavioral intention (thus supporting H11c), but have no moderating effect on other antecedents. This is different from UTAUT2, in which gender does moderate the relationship between facilitating conditions and behavioral intention. However, the results of the present study are similar to findings in the field of psychology. Eagly (1983) found that men are more influential and women are more easily influenced. We suggest that this difference may be caused by a difference in the research context. Since cloud based e-invoicing is a relatively new e-government application in Taiwan, opinions from friends and family members will affect users’ decisions regarding their use of the service. However, the degree of social influence on each gender varies. Therefore,

Table 7 Discriminant validity of the constructs.

PE EE SI FC Trust Sec PR BI

Mean

S.D.

PE

EE

SI

FC

Trust

Sec

PR

BI

3.68 3.74 3.15 3.27 2.97 3.61 3.49 3.61

0.93 0.87 1.01 0.92 0.93 0.77 0.94 1.11

0.89 0.70 0.57 0.50 0.54 (0.15) (0.04) 0.61

0.88 0.64 0.67 0.54 (0.24) (0.12) 0.67

0.88 0.49 0.51 (0.18) (0.13) 0.60

0.81 0.47 (0.29) (0.15) 0.56

0.85 (0.31) (0.25) 0.55

0.71 0.38 (0.29)

0.94 (0.23)

0.97

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Table 8 Overall analysis. Hypotheses

Path coefficient (ˇ)

t-value

Support

H1: Performance expectation → Intention H2: Effort expectation → Intention H3: Social influence → Intention H4: Facilitating conditions → Intention H5: Trust → Perceived risk H6: Security concerns → Perceived risk H7: Security concerns → Trust H8: Trust → Intention H9: Security concerns → Intention H10: Perceived risk → Intention H11: Gender moderating effect H11a: Gender × Performance expectation H11b: Gender × Effort expectation H11c: Gender × Social influence H11d: Gender × Facilitating conditions H11e: Gender × Trust H11f: Gender × Perceived risk H11 g: Gender × Security concerns H12: Age moderating effect H12a: Age × Performance expectation H12b: Age × Effort expectation H12c: Age × Social influence H12d: Age × Facilitating conditions H12e: Age × Trust H12f: Age × Perceived risk H12g: Age × Security concerns

0.10 0.32 0.27 0.06 (0.14) 0.33 (0.30) 0.19 (0.02) (0.19)

1.13 3.12** 3.21** 0.51 1.84 5.12*** 4.51*** 2.05* 0.28 2.98**

No Yes Yes No No Yes Yes Yes No Yes

0.47 0.49 (0.45) 0.10 (0.19) 0.32 (0.01)

1.59 1.35 2.05* 0.30 0.87 1.86 0.02

No No Yes No No No No

0.09 0.10 (0.08) 0.04 (0.07) (0.12) (0.12)

1.01 1.08 0.77 0.53 0.94 2.09* 1.72

No No No No No Yes No

* ** ***

p < 0.05. p < 0.01. p < 0.001.

both social influence and its gender moderating effect are significant. Based on the above findings, we suggest that community promotions are required, and will have a greater effect on females. Governments can devote money and effort to promotion activities, such as activities on virtual communities, especially for females. Age level was another moderating variable investigated in this study. Our results also differed from UTAUT2, in which age moderates the relationship between facilitating conditions and behavioral intention. In this study, the moderating effect of age exists only in the relationship between perceived risk and behavioral intention (supporting H12f). In other words, the negative effect of perceived risk on cloud based e-invoice adoption is more significant for older people. Regarding facilitating conditions, since the mobile device is the major platform for e-invoicing in Taiwan, we find it notable that Conci, Pianesi, and Zancanaro (2009) indicated no significant differences in mobile phone use behaviors among younger and older users. Additionally, unlike information systems in general, cloud based e-invoicing is a seamless part of the transaction, so both younger and older users can use it without

additional help. Therefore, age level has no moderating effect on the relationship between facilitating conditions and behavior intention. Our results show that age level does moderate the relationship between perceived risk and behavioral intention toward cloud based e-invoicing. This is similar to Lian and Yen (2014) in that the risk barrier varies across different age levels, in the context of online shopping. Based on this finding, we suggest that governments must consider age differences when promoting e-invoice services, especially in regard to any risk concerns related to e-invoice adoption. For example, citizens will be concerned that businesses may not actually release the invoice when using a paperless e-invoice. Citizens may also be concerned that lottery award money may not be transferred correctly to the appropriate account when using a paperless e-invoice. Regarding the context specific variables proposed in the study, data analysis results indicate that trust in e-government and perceived risk significantly and directly affect behavioral intention (supporting both H8 and H10). However, our results were unexpected, since previous studies (Chang & Chen, 2008; Kim et al.,

Table 9 Overall results of the hypothesis testing. Hypotheses

Results

H1: Performance expectation will positively affect users’ intentions to use cloud based e-invoice services. H2: Effort expectation will positively affect users’ intentions to use cloud based e-invoice services. H3: Social influence will positively affect users’ intentions to use cloud based e-invoice services. H4: Facilitating conditions will positively affect users’ intentions to use cloud based e-invoice services. H5: Trust in e-government will negatively affect perceived risk regarding the use of cloud based e-invoice services. H6: Security concerns regarding e-government will positively affect perceived risk regarding the use of cloud based e-invoice services. H7: Security concerns regarding e-government will negatively affect trust in e-government regarding the use of cloud based e-invoice services. H8: Trust in e-government will positively affect users’ intentions to use cloud based e-invoice services. H9: Security concerns regarding e-government will negatively affect users’ intentions to use cloud based e-invoice services. H10: Perceived risk will negatively affect users’ intentions to use cloud based e-invoice services. H11: Gender differences will moderate the relationships between antecedent variables and users’ intentions to use cloud based e-invoice services. H12: Age level will moderate the relationships between antecedent variables and users’ intentions to use cloud based e-invoice services.

Non-supported Supported Supported Non-supported Non-supported Supported Supported Supported Non-supported Supported Partial supported Partial supported

J.-W. Lian / International Journal of Information Management 35 (2015) 98–109

2008; Shin, 2013; Warkentin et al., 2002) showed that security concerns affect behavioral intention directly (hence, the lack of support for H9). The results of this current study indicate that trust in e-government and perceived risk serve as mediators between security concerns regarding e-government and behavioral intention (supporting H6 and H7). Those relationships are indirect. Surprisingly, trust in e-government does not affect perceived risk directly, as inferred (H5 was not supported). One possible explanation is that trust in e-government is a multi dimensional construct that applies to more than just e-invoicing, while the perceived risk factor is mainly focused on e-invoice adoption. Thus, the causal relationship lacks support. These are important findings which can significantly impact decisions regarding e-government system design and program promotions. Besides, these dimensions are not mentioned in the UTAUT2 but are critical factors for cloud based einvoice services. Thus, they are crucial context variables for cloud based e-government services in general and can serve as context variables for future research. This study also proposed trust in egovernment and security concerns regarding e-government as two critical antecedents of perceived risk in this context. The analysis results indicate that these two variables explain only 16% of the variance (Table 5). Further study is needed to advance our understanding of the antecedents of perceived risk. Security concerns were also found to significantly affect perceived risk. Table 4 shows that the female sample had the lowest level of trust in egovernment (mean = 2.88). For males, the value for this variable was also relatively low, compared to that of the other variables. This phenomenon is particularly important for governments implementing electronic services. 7. Limitations There are four main limitations in this study. First of all, this study focused exclusively on cloud based e-invoice services. Future studies can apply our model across different e-government services and compare the similarities and differences to make further contributions. Second, only data from Taiwan was employed in this study. We suggest that samples be drawn from different countries in order to understand the effects of culture differences. Third, the antecedents of perceived risk regarding cloud based e-government need further investigation. In this study, the two variables we employed (trust in e-government and security concerns regarding e-government) provide only 16% of the explanatory power. Therefore, more critical variables and theories are required to advance this investigation. Finally, respondents to the online survey may cause sample bias, limiting the generalizability of the findings. However, this is a common limitation of online surveys (Teo, 2001). 8. Implications The results of this study have academic and practical implications and make contributions to both the government and academia. Implications for practice and theory are discussing in the following sections. 8.1. Implications for practice The popularity of cloud computing and mobile devices is prompting an increasing number of governments around the world to provide cloud based services. They can refer to the results of this study when designing and promoting their services. For example, governments can design different promotional activities for different users. The Taiwanese government has invested money in an e-invoice lottery to encourage citizens to adopt e-invoices. People who receive e-invoices will have extra opportunities to win the lottery.

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Although the e-invoice usage rate is rising year by year (Central News Agency, 2014), recent news also indicates that many people still refuse to use e-invoicing because of security and risk concerns (Liberty Times, 2014). As of the date of this publication, the current paperless e-invoice usage rate is only 8% (Liberty Times, 2014). Given this new data, policy makers around the world should be aware that if spending money to foster e-invoice adoption to increase taxes effectiveness is really necessary. The Taiwanese government has invested a lot of money in a lottery to increase the e-invoice usage rate, but many citizens still refuse to use e-invoice. Therefore, in addition to the lottery, the government must again take into consideration its citizens’ major concerns and the requirements of businesses if its new policy is to be successful. The results of this study provide relevant information for governments and policy makers. 8.2. Implications for theory As far as academia is concerned, little research has been done on cloud based e-government services. The results of this study can be referenced by future studies. We found that while UTAUT and UTAUT2 related variables must be included, context variables related to the distinguishing characteristics of the specific information technology application must also be included in the study. This study has three major implications for theory development. First, we confirmed three context variables (trust in e-government, security concerns regarding e-government, and perceived risk) which are critical to the adoption of cloud based e-invoice services. Second, performance expectation and facilitating conditions are not found to be significant in this study. Third, the only relationship moderated by gender differences is the one between social influence and behavioral intention. The moderating effect of age affected only the relationship between perceived risk and behavioral intention. Overall, the above results contribute to the research streams regarding the employment of UTAUT and UTAUT2 in different research contexts, and cloud based e-government services in particular. 9. Conclusions Cloud based e-government is the current trend in online information services. Previous literature on e-government adoption has focused on traditional web based online services. However, given the distinguishing characteristics and new business model of cloud computing, more study of cloud based e-government services is warranted. It was previously unknown whether the results of existing studies on the adoption of traditional web based e-government services could be applied to cloud based e-government services. To fill this gap, this current study integrated UTAUT2 with the distinguishing features of cloud computing to propose an integrated model to understand the above issues. The results are summarized in Fig. 3 and Table 9. The critical factors are effort expectation, social influence, trust in e-government, and perceived risk. Security concerns regarding e-government negatively affect trust in egovernment and positively affect perceived risk. We also found that gender differences moderate the relationship between social influence and behavioral intention. Age level was found to moderate the relationship between perceived risk and behavioral intention. Overall, this study contributes to the understanding of the critical factors for the adoption of novel cloud based e-government services, and enriches the existing literature for both academics and practitioners. The results also indicate the similarities and differences in the adoption of cloud based e-government services, which diverge from previous literature and can be used as a reference for government policy development.

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Acknowledgement The author would like to thank the Ministry of Science and Technology of Republic of China, Taiwan, for financially supporting this research under contract No. NSC 102-2410-H-025-027. Appendix A. Measurement Items Performance expectation (adapted from Davis, 1989; Venkatesh et al., 2003; Walczuch, Lemmink, & Streukens, 2007) 1. 2. 3. 4. 5. 6.

The e-invoice makes transactions happen more quickly. The e-invoice improves employee performance. The e-invoice makes transactions more productive. The e-invoice makes transactions more efficient. The e-invoice makes transaction more convenient. Overall, the e-invoice is useful.

Effort expectation (adapted from Davis, 1989; Venkatesh et al., 2003; Walczuch et al., 2007) 1. 2. 3. 4. 5. 6.

Learning to use the e-invoice is easy. Using the e-invoice is easy. The process for using the e-invoice is clear. Using the e-invoice is not a burden during the transaction. Remembering how to use the e-invoice is easy. Overall, the e-invoice is easy to use. Social influence (adapted from Venkatesh et al., 2003)

1. My friends think that I should use e-invoices. 2. A person who is very important to me believes that I should use e-invoices. 3. Overall, many of my friends use e-invoices. Facilitating conditions (adapted from Venkatesh et al., 2003) 1. I have the hardware and software for e-invoices. 2. I have the skill and knowledge for e-invoices. 3. The experience of using an e-invoice is similar to using the Internet. 4. When I have problems using an e-invoice, someone can help me solve them. Trust in e-government (adapted from Pavlou & Gefen, 2004) 1. 2. 3. 4.

We can always trust government e-invoice services. E-invoice implementation is the right government policy. Government e-invoice services have high integrity. The government has the professional knowledge and skills for e-invoicing.

Security concerns regarding e-government (adapted from Parasuraman, 2000) 1. 2. 3. 4. 5.

Using credit cards online is not secure. Online financial activities are not secure. I will worry about information security when transferring online. I have no confidence in online transactions. After an online transaction, double checking the paper documents double is required. 6. Any automatic operation still needs to be double checked after an online transaction. 7. Face to face interactions are important when dealing with transactions. 8. I like to interact with people instead of computers when I need assistance.

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