Intention to Use Cloud Accounting System among SMEs in

The Small and medium enterprises (SMEs) are considered as significant source of national and local economic development in many countries (Wan Ismail ...
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Proceedings of The 6th International Seminar on Entrepreneurship and Business (ISEB 2018) November 24th, 2018. Kota Bharu, Kelantan

Intention to Use Cloud Accounting System among SMEs in Malaysia Zul Karami Che Musa1 , Mohd Nazri Muhayiddin1, Mohd Nor Hakimin Yusoff1, Mohammad Ismail1, Mahathir Muhamad1 1 Faculty of Entrepreneurship and Business, Universiti Malaysia Kelantan, Malaysia Email: [email protected]; [email protected]; [email protected]; [email protected]; [email protected] Abstract - This study explores the underlying factors among SMEs that explain their intentions to use cloud accounting system in Malaysia. This study is motivated by the fact that SMEs are considered as significant economic players and an influential source of national and local economic growth. However, looking at high failure rate for Malaysian SMEs, more research should concern on supporting their business operation especially with regard to accounting or financial management. Based on the literature on this field as well as the concept in Unified Theory of Acceptance and Use of Technology (UTAUT) this study establishes a conceptual model that incorporate additional perceived security to the original four explanatory variables of UTAUT: performance expectancy, effort expectancy, social influence and facilitating condition. From the result, variable of effort expectancy, social influence and facilitating condition are proved significant whereas performance expectance and perceived security is not significant. This paper provides cost benefit proposition which should be SMEs considered in order to move their business to the next level. Keywords: accounting, cloud accounting, UTAUT, SMEs.

1.

Introduction

The Small and medium enterprises (SMEs) are considered as significant source of national and local economic development in many countries (Wan Ismail & Mokhtar, 2016) and constitute more than 90% of total business establishment in many countries. In Malaysia, SMEs had been regarded as the backbone of Malaysian economy as they offer huge employment opportunities, around 57.5% of the whole employment market, contributed 33.1% of the gross domestic product (Hashim, 2015). However, SMEs face common difficulties which threaten both their survival rate and performance. Past statistics indicated that the estimated failure rate for Malaysian SMEs was 60 per cent (Wan Ismail & Mokhtar, 2016). Many researchers (e.g. Lussier & Halabi, 2010; Stokes & Blackburn, 2002) agreed that accounting record is one of the factors that important in determining the success or failure of the SMEs. Despite the great benefits of maintaining accounting records, previous research however found that most of smaller firms do not keep them, hence fail to produce systematic accounting reports (Dyt & Halabi, 2007). The most efficient technique to report accounting data is by using accounting software or accounting information system (AIS). There are several researches investigating AIS adoption among SMEs (Ali, Rahman, & Ismail, 2012; Wan Ismail & Mokhtar, 2016), 800

Proceedings of The 6th International Seminar on Entrepreneurship and Business (ISEB 2018) November 24th, 2018. Kota Bharu, Kelantan

however AIS adoption among SMEs with specific reference to cloud technology in Malaysia is almost non-existence.main body of all papers should commence with an introduction section which should explain the background and purpose of the paper. Formatting is described in section 2 below. 2.

Literature Review

The added value of accounting information system (AIS) to SMEs includes the ability to simplify the accounting record as compared to manual accounting record but required great amount of investment, training and involve skilled accountants. It is the challenge that the entry, interpretation and auditing of accounting data is extremely dependent on understanding how accounting software works. With reference to AIS generations by Phillips (2012) and other recent research, the upto-date trend concerning the information system specifically in the field of accounting is the cloud computing technology, based on which the following terms derive namely cloud accounting, e-accounting, real-time accounting and online accounting (Dimitriu & Matei, 2015; B. Ionescu, Ionescu, Tudoran, & Bendovschi, 2013). 2.1

Cloud Accounting

This paper inclines to choose straight forward definition by Mihai (2015) which state cloud accounting as “an accounting software product which can be accessed anytime and from any place with an internet connection, and which does not require previous installation or management or its own servers” (Mihai, 2015). There is a continuous increase in literature and practice of finance and accounting in the cloud (Brandas, Megan, & Didraga, 2015). Ionescu et al. (2013) stated unlike traditional desktop accounting software, cloud accounting software are not owned by the organization, but involves hardware and maintenance costs, unlimited number of users (Bosoteanu, 2016). 2.2

Advantage of Accounting in Cloud

The major motivation for the worldwide adoption of cloud accounting is the economic benefit that cuts expenses using an external data centre where these applications are stored makes the purchase of additional hardware and software no longer necessary (Arsenie-Samoil, 2011; Gangwar, Date, & Ramaswamy, 2015; Tarmidi, Rasid, Alrazi, & Roni, 2014). By having cloud accounting, companies could save the cost with regard to maintenance cost as well as lack of additional cost (Belfo & Trigo, 2013; Brandas et al., 2015; B. Ionescu et al., 2013). Moreover, this cloud technology could provide access to financial information without geographical limitations. This means that different type of stakeholders can have an access to accounting data with real time reporting from anywhere and anytime (Belfo & Trigo, 2013). Thus, this will improve communication and collaboration between users of financial information and subsequently boost the confidence of potential investors or bankers (Bosoteanu, 2016; Dimitriu & Matei, 2014). With regard to cloud facility, this technology enjoys high capability with unlimited storage and also scalability which depends on subscription package (Gangwar et al., 801

Proceedings of The 6th International Seminar on Entrepreneurship and Business (ISEB 2018) November 24th, 2018. Kota Bharu, Kelantan

2015). Having said this, there will less hurdle in case SMEs transform into big company, it could scale the capacity according to the need of the organizational size. 2.3

Security Challenges of Cloud Accounting

The first and major concern with cloud accounting is the issue of security since accounting process involves storing sensitive and confidential data (Bosoteanu, 2016; Brandas et al., 2015; B. S. Ionescu & Prichici, 2013; Molnar & Schechter, 2010). The stored data or information in AIS is crucial for SMEs such. From legal perspective, the firm does not owns the infrastructure offered by the cloud service provider except for the data entered and thus, has less control over the processing systems as well as storage of the data (Brandas et al., 2015). Ionescu & Prichici (2013) reported that based on study by Information Systems Audit and Control Association (ISACA), small companies have low interest to use cloud accounting compared to bigger companies. One of the main obstacles is the security issues especially when it comes to consideration of migration to this new technology. Furthermore, the threat of security also concerns the protection of customer financial information and susceptible to unauthorized access (Dimitriu & Matei, 2014). Associated to security problems is the issue of privacy specifically the concern over sensitive and confidential information (Brandas et al., 2015; Dimitriu & Matei, 2014). It is also pointed out that in order to ensure this technology runs its intended functions, it implies a strong, stable connection to the internet, which not majority of SMEs enjoys this kind of facility (Bosoteanu, 2016). 2.4

Unified Theory of Acceptance and Use of Technology (UTAUT)

Many theoretical models have been used to study the adoption of technological innovations among SMEs such as the Technology Acceptance Model (TAM), Diffusion on Innovation (DOI) theory, the Theory of Planned Behaviour (TPB) and so on. After reviewing those theories, the Unified Theory of Acceptance and Use of Technology (UTAUT) is regarded very comprehensive theory since it was developed through a review and consolidation of the constructs of eight models of earlier research (Venkatesh, Morris, Davis, & Davis, 2003). Though some may claim UTAUT is more appropriate at individual level (Oliveira & Martins, 2011; Wan Ismail & Mokhtar, 2016), it is however also being used at firm level in mainstream literature (e.g Anderson & Schwager, 2004; Kumar, 2012). As for basic concept, UTAUT has four key constructs as in Table 1, which affects behavioral intention to use a technology. With reference to UTAUT, performance expectancy, effort expectancy, and social influence give effect to behavioral intention to use a technology, while behavioral intention and facilitating conditions have direct effect to technology use. Table 1. UTAUT’s four key constructs that influence behavioral intention to use a technology Construct Performance expectancy

Definition “…the degree to which an individual believes that using the system will help him or her to attain gains in job performance.” (Venkatesh et al., 2003,

802

Constructs of Earlier Research • perceived usefulness (Davis, 1989) • extrinsic motivation (Davis, Bagozzi, & Warshaw, 1992)

Proceedings of The 6th International Seminar on Entrepreneurship and Business (ISEB 2018) November 24th, 2018. Kota Bharu, Kelantan

p. 447) Effort Expectancy Social Influence

Facilitating Conditions

3.

•

“…the degree of ease associated with • the use of the system.” (Venkatesh et • al., 2003, p. 450) • “…the degree to which an individual • perceives that important others believe he or she should use the new system.” • (Venkatesh et al., 2003, p. 451) “…the degree to which an individual • believes that an organizational and technical infrastructure exists to • support use of the system.” (Venkatesh et al., 2003, p. 453) •

job-fit (Thompson, Higgins, & Howell, 1991) perceived ease of use (Davis et al., 1992) complexity (Thompson et al., 1991) ease of use (Moore & Benbasat, 1991). subjective norm in the TRA (Davis, Bagozzi, & Warshaw, 1989) social factors in the model of personal computer utilisation (Thompson et al., 1991) perceived behavioural control (Ajzen, 1991; Taylor & Todd, 1995) facilitating conditions (Thompson et al., 1991) compatibility (Moore & Benbasat, 1991)

Research Methodology

The research methodology has been centered on the already identified existing core variables. Hence a simple direct relationship of these core variables has been used to create the research model to understand which of them is the most dominant. To further quantify, a detailed questionnaire was used to gather the formal data (primary data) from the various small and medium enterprise, primarily based in Malaysia. Finally, data collected from the final survey was analyzed. For statistical analysis, SmartPLS (a structural model based tool) was used to build, run and validate the process model. Partial least square (PLS) regression techniques were used to analyze the latent constructs. SmartPLS exhibits both the measurement model (outer model) and the structural model (inner model). 3.1

Conceptual Research Framework

This paper adopts the UTAUT model where the element of perceived security is put into account as additional independent variable as in Figure 1. With regard to moderating variable, since the focus of the study is SMEs i.e. firm level, gender variable is dropped and replaced by organizational size. Voluntariness of use is also removed since there is no mandatory requirement imposed by Malaysian government or investors to use cloud accounting. 3.1.1

Perceived Security

This construct is selected due to the nature of cloud accounting involves highly confidential data and users need to maintain certain level of security if they agree to accept the system. This construct has been used in other revised technology adoption theory (e.g. Cheng, Lam, & Yeung, 2006; Lallmahamood, 2007). Having reviewed previous literature, this paper define perceived security as the extent to which a user feel protected against security threats with regard information processed in the system when he or she accept the new technology (Cheng et al., 2006). 803

Proceedings of The 6th International Seminar on Entrepreneurship and Business (ISEB 2018) November 24th, 2018. Kota Bharu, Kelantan

3.2

Hypothesis Development

Performance expectancy means that an individual believes that using the system will help them to attain gains in job performance. The others constructs from the different models that pertain to performance expectancy are perceived usefulness (Davis, 1989), extrinsic motivation (Davis et al., 1992), job-fit (Thompson et al., 1991). Most of these research view that performance expectancy has positive influence to behavioral intention to adopt technology. Hypothesis H1: Performance expectancy has positive effect on intention of using cloud accounting. Effort expectancy indicates that the degree of ease associated with the use of the system. The other constructs from the existing models capture the concept of effort expectancy are perceived ease of use (Davis et al., 1992), complexity (Thompson et al., 1991) and ease of use (Moore & Benbasat, 1991). Most of those research view that effort expectancy gives positive influence to behavioral intention to adopt technology. Hypothesis H2: Effort expectancy has positive effect on intention of using cloud accounting. Facilitating condition is the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of the system. The other constructs from the existing models capture the concept of facilitating condition are perceived behavioural control (Ajzen, 1991; Taylor & Todd, 1995), facilitating conditions (Thompson et al., 1991) and compatibility (Moore & Benbasat, 1991). From existing research, it is being found that facility condition gives positive influence to behavioral intention to adopt technology. Hypothesis H3: Facility condition has positive effect on intention of using cloud accounting. Social Influence is the degree to which an individual perceives that important others believe people should use the new system. Social influence as a direct determinant of behavioral intention is represented as subjective norm in the TRA (Davis et al., 1989), social factors in the model of personal computer utilisation (Thompson et al., 1991). Most of those research view that social influence gives positive influence to behavioral intention to adopt technology. Hypothesis H4: Social influence has positive effect on intention of using cloud accounting. Perceived security is the extent to which a user feel protected against security threats with regard information processed in the system. This construct has been used in other revised technology adoption theory (e.g. Cheng, Lam, & Yeung, 2006; Lallmahamood, 2007). From those research, it is being found that security is very important and gives positive influence to behavioral intention to adopt technology. Hypothesis H5: Perceived security has positive effect on intention of using cloud accounting. 804

Proceedings of The 6th International Seminar on Entrepreneurship and Business (ISEB 2018) November 24th, 2018. Kota Bharu, Kelantan

4.

Result

From the data collection, a total of 119 questionnaires had been answered. A total of 19 questionnaires could not be used due to incomplete and flat answers. The analysis is performed using partial least square analysis through SmartPLS software version 3.2.6. 4.1

Demographic Analysis

Respondents had good computing experiences, since all them answer the questionnaire online through link distributed in Facebook webpage. This study successfully engages users who are actively involved in small medium enterprise which are in wholesale and retail industry followed by business services and healthcare. Most respondents' business form are sole proprietorship (61%) followed by limited partnership (26%), partnership (10%) and private limited company (3%). In terms of location, they are mostly from Selangor (22%) followed by Kelantan and Kuala Lumpur 17% respectively, Johor 10% and the rest of the states in Malaysia range from 1 to 6% as shown in Table 2 below. This indicates that this research had been successfully access the appropriate respondents that are owners or managers of SME that are competent on using computers. Table 2 – Respondent distribution based on states in Malaysia

States Selangor Kelantan Kuala Lumpur Johor Kedah Perak Putrajaya 4.2

% 22% 17% 17% 10% 6% 6% 1%

States Sarawak Terengganu Sabah Penang Melaka Pahang

% 6% 6% 4% 3% 1% 1%

Validity and Reliability Test

Validity and Reliability Measurements Model Testing were made by seeing The Overview of PLS Algorithm Results (see Table 3) and a score of outer loading (see Table 4) that had been generated by Smart PLS 3.2.6. Validity is done by looking at the scores of AVE and Loading Scores in the Output PLS Algorithm. For Convergent Validity Test, AVE Scores should be above 0.5. Similarly, the Discriminant Validity Test, Loading Scores should be more than 0.7. Table 3 shows that the AVE scores entirely already above 0.6. Table 4 shows that for the all indicators, Loading Score has been above 0.7 and appear to accumulate in their corresponding variables. Based on these descriptions, the researchers concluded that this study measurement models have passed the test of validity. Reliability Test of Measurements Model was made by seeing The Cronbach's Alpha Scores and Composite Reliability of Output PLS algorithm and should be above 0.7. Output PLS Algorithm Based on Table 3, all the latent variables show Scores Cronbach's Alpha and Composite Reliability above 0.7. This shows that all the latent variables have passed the reliability test. 805

Proceedings of The 6th International Seminar on Entrepreneurship and Business (ISEB 2018) November 24th, 2018. Kota Bharu, Kelantan

Table 3 - The Overview of PLS Algorithm Result

BI PE EE FC SI PS

AVE 0.758 0.695 0.69 0.702 0.634 0.682

Composite Reliability R Square 0.904 0.566 0.872 0.869 0.876 0.873 0.865

Cronbach’s Alpha 0.84 0.78 0.776 0.788 0.806 0.765

Table 4 - Outer Loading Scores Results

BI PE EE BI1 0.817 BI2 0.89 BI3 0.902 PE1 0.853 PE2 0.781 PE3 0.865 EE1 0.876 EE2 0.86 EE3 0.75 FC1 FC2 FC3 SI1 SI2 SI3 SI4 PC1 PC2 PC3 4.3

FC

SI

PC

0.832 0.824 0.857 0.763 0.829 0.857 0.729 0.85 0.881 0.741

Hypothesis Testing

Hypothesis testing is done by looking at the value of t-statistics in Bootstrapping Results (see Table 5). The hypothesis is accepted if the t-statistics in bootstrapping showed scores above 1.96 or p-value less than 0.05. For that, the most accepted hypothesis in this study is H2, H3, H4 while for hypothesis H1 and H5 rejected. Based on Table 5 and Figure 2, the most powerful construct that influence on Behavior Intention of Using Cloud Accounting is Facility Condition (FC) with the effect of 39.6%, which is then followed by the construct of Social Influence (35.3%) and Effort Expectancy (29.3%). 806

Proceedings of The 6th International Seminar on Entrepreneurship and Business (ISEB 2018) November 24th, 2018. Kota Bharu, Kelantan

Table 5 – Bootstrapping Result Sample Mean (M) 0.12

Standard Deviation (STDEV) 0.101

Path Coefficients

T Statistics (|O/STDEV|)

P Values

H1: PE-->BI

Original Sample (O) 0.119

0.075

1.184

0.236

Accepted / Rejected Rejected

H2: EE-->BI

0.246

0.235

0.102

0.293

2.406

0.013

Accepted

H3: FC-->BI

0.248

0.25

0.1

0.396

2.475

0.011

Accepted

H4: SI-->BI

0.279

0.286

0.097

0.353

2.867

0.005

Accepted

H5: PS-->BI

0.04

0.043

0.121

-0.15

0.333

0.743

Rejected

5.

Discussion

This study aims to examine the determinants of behavior intention to accept cloud accounting by integrating relevant constructs of UTAUT. Research conducted against SMEs so that this research can discover what factors influence a user intention to accept cloud accounting in their operation. User acceptance to cloud accounting will always be shown with the user behavior intention to use it. This study found that behavioral intention of cloud accounting acceptance is influenced by effort expectancy, facility condition and social influence. Performance expectancy and perceived security is not shown to have an influence on the behavioral intention of cloud accounting acceptance. 5.1

Performance expectancy

This study found a quite different result from other studies that also use UTAUT that performance expectancy does not influence the user intention to accept cloud accounting. This finding contrasts with the findings of Venkatesh et al. (2003), Mathur & Dhulla (2014), Nguyen et al. (2014) Vatanasakdakul et al. (2010). This result is not showing that cloud accounting does not contribute to performance but rather refer specifically to attitude of recording accounting data is low among SMEs. It may be due to the respondents are mostly small business and they do not make use of historical accounting data to perform analysis in order to improve the performance of the business. It can be assumed that SMEs do not have the complete idea how cloud accounting give added value in terms of performance as compared to traditional desktop accounting. In addition, this result can be explained by the findings of Dyt and Halabi (2007) stated that performance does not show the effect on intention because the majority of respondents are relatively SMEs so do not consider the aspects of the performance of cloud accounting as an important thing that can influence their intention toward the adoption of cloud technology. 5.2

Effort expectancy

Effort expectance is related with ease of use, existing research study supports the same (Venkatesh & Morris, 2000; Venkatesh, Morris, & Ackerman, 2000; Vankatesh et al., 2003; Mathur & Dhulla, 2014; Vatanasakdakul et al. 2010). Analysis shows that effort expectance is third influential variable amongst SMEs which relates to flexibility and availability. Cloud accounting allows user to access from anywhere-any time. On 807

Proceedings of The 6th International Seminar on Entrepreneurship and Business (ISEB 2018) November 24th, 2018. Kota Bharu, Kelantan

demand nature of cloud accounting, influences usage of technology and intention of using technology. Cloud computing service providers provide ready to use application/ software, which significantly impacts on behavior intention and usage. 5.3

Facility Condition

The analysis shows that facility condition is the most important factors that influence intention to use cloud accounting. The positive relationship from the result is in line with previous findings (e.g. Venkatesh et al., 2003; Mathur & Dhulla, 2014; Nguyen et al., 2014; Vatanasakdakul et al., 2010). This contract denotes that excellent facility condition is crucial in order to attract users to adopt cloud accounting technology. 5.4

Social Influence

The existence of peers, friends and family influence in the cloud technology also influence user intention to use cloud accounting in their industries. The role of the social system to behavior intention are consistent with research of Venkatesh et al. (2003), Mathur & Dhulla (2014), Nguyen et al. (2014) Vatanasakdakul et al. (2010). These findings confirm the role of social influence on the individual intention that use technology in an organization as described by Lam et al. (2007) concerning the exclusion of social influence in UTAUT model by Vankatesh et al. (2003). 5.5

Perceived Security

Perceived security is the feeling of protection against security threats. Security especially with regard to privacy and data protection is the core issue in all the challenges of cloud accounting. The result shows insignificant relationship, this construct does not influence the user intention to accept cloud accounting. This result is contrast to previous research that support this variable in influencing intention to use cloud accounting (e.g. Bosoteanu, 2016; Brandas et al., 2015; Ionescu & Prichici, 2013; Molnar & Schechter, 2010).

This indicates that confident level of respondents with regard to security of cloud accounting is still not reaching the desired level. Even though the security measure taken by the cloud service provider is among the highest standard, but due to lack of awareness of business community, people still miss this factor out in motivating adoption of cloud accounting. 6.

Conclusion

The study illustrates that all scales of independent variables with relation to intention to use cloud accounting. Partial least square regression analysis indicates that measurement scales for all constructs are satisfactory. The results also provide that are relationships between the effort expectancy, facility condition, social influence and cloud accounting intention. The performance expectancy and perceived security are non-significant with cloud accounting. Hence, there are three out of five hypotheses are supported in this research. 808

Proceedings of The 6th International Seminar on Entrepreneurship and Business (ISEB 2018) November 24th, 2018. Kota Bharu, Kelantan

Cloud accounting is a dynamic platform, built from core components that include resources, management of resources and technical competence to build and deploy services. It can be conferred from the most dominant factor i.e. facility condition, the service depends good facility such as superb server technology as well as the speed of the internet. Cloud accounting adoption provides SMEs cost benefit proposition which should be considered in order to move their business to the next level. This study contributes knowledge among the researchers and is an attempt to highlight the factors influencing the adoption of cloud accounting among SMEs in Malaysia. 7.

References

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. Ali, A., Rahman, M. S. A., & Ismail, W. (2012). Predicting Continuance Intention to Use Accounting Information Systems Among SMEs in Terengganu, Malaysia. International Journal of Economics and Management, 6(2), 295–320. Anderson, J. E., & Schwager, P. H. (2004). SME adoption of wireless LAN technology: applying the UTAUT model. In Proceedings of the 7th annual conference of the southern association for information systems (Vol. 7, pp. 39–43). Arsenie-Samoil, M. D. (2011). Cloud Accounting. Ovidius University Annals, Economic Sciences Series, 2, 782–787. Belfo, F., & Trigo, A. (2013). Accounting information systems: Tradition and future directions. Procedia Technology, 9, 536–546. Bosoteanu, M. (2016). Cloud Accounting In Romania. A Literature Review. Risk in Contemporary Economy, 400–405. Retrieved from https://ideas.repec.org/a/ddj/fserec/y2016p400-405.html Brandas, C., Megan, O., & Didraga, O. (2015). Global perspectives on accounting information systems: mobile and cloud approach. Procedia Economics and Finance, 20, 88–93. Cheng, T. C. E., Lam, D. Y. C., & Yeung, A. C. L. (2006). Adoption of internet banking: an empirical study in Hong Kong. Decision Support Systems, 42(3), 1558–1572. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982–1003. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace1. Journal of Applied Social Psychology, 22(14), 1111–1132. Dimitriu, O., & Matei, M. (2014). A New Paradigm for Accounting through Cloud Computing. Procedia Economics and Finance, 15(14), 840–846. https://doi.org/10.1016/S2212-5671(14)00541-3 Dimitriu, O., & Matei, M. (2015). Accounting in the Cloud. In Management, Knowledge and Learning Joint International Conference (pp. 1259–1265). Dyt, R., & Halabi, A. K. (2007). Empirical Evidence Examining the Accounting Information Systems and Accounting Reports of Small and Micro Business in Australia. Small Enterprise Research, 15(2), 1–9. https://doi.org/10.5172/ser.15.2.1 809

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Gangwar, H., Date, H., & Ramaswamy, R. (2015). Understanding determinants of cloud computing adoption using an integrated TAM-TOE model. Journal of Enterprise Information Management, 28(1), 107–130. Hashim, H. (2015). SME Development Framework?: the Malaysian Case. In Cairo AMC-CBE-WBG SME Conference (pp. 1–22). Ionescu, B., Ionescu, I., Tudoran, L., & Bendovschi, A. (2013). Traditional accounting vs. Cloud accounting. In Proceedings of the 8th International Conference ACCOUNTING AND MANAGEMENT INFORMATION SYSTEMS, AMIS (pp. 106–125). Ionescu, B. S., & Prichici, C. (2013). Potential Beneficiaries of Cloud Accounting technology: small or large Companies? Kumar, K. (2012). Information System Adoption And Usage: Validating UTAUT Model For Bangladeshi SMEs. International Journal of Management, IT and Engineering, 2(6), 402–416. Lallmahamood, M. (2007). An Examination of Individual’s Perceived Security and Privacy of the Internet in Malaysia and the Influence of this on their Intention to Use E-commerce: Using an Extension of the Technology Acceptance Model. Journal of Internet Banking and Commerce, 12(3), 1. Lussier, R. N., & Halabi, C. E. (2010). A three-country comparison of the business success versus failure prediction model. Journal of Small Business Management, 48(3), 360–377. Mathur, S. K., & Dhulla, T. V. (2014). Factors influencing professionals decision for cloud computing adoption. Mihai, G. (2015). Cloud ERP and Cloud Accounting Software in Romania. Fascicle I. Economics and Applied Informatics, 61–66. Molnar, D., & Schechter, S. E. (2010). Self Hosting vs. Cloud Hosting: Accounting for the Security Impact of Hosting in the Cloud. In WEIS. Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. Nguyen, T. D., Nguyen, D. T., & Cao, T. H. (2014). Acceptance and Use of Information System: E-Learning Based on Cloud Computing in Vietnam. In ICT-EurAsia (pp. 139–149). Oliveira, T., & Martins, M. (2011). Literature review of Information Technology Adoption Models at Firm Level. Electronic Journal of Information Systems Evaluation Volume, 14(1), 110–121. Phillips, B. A. (2012). How the Cloud Will Change Accounting Forever. Stokes, D., & Blackburn, R. (2002). Learning the hard way: the lessons of ownermanagers who have closed their businesses. Journal of Small Business and Enterprise Development, 9(1), 17–27. Tarmidi, M., Rasid, S. Z. A., Alrazi, B., & Roni, R. A. (2014). Cloud computing awareness and adoption among accounting practitioners in malaysia. ProcediaSocial and Behavioral Sciences, 164, 569–574. Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144–176. Thompson, R. L., Higgins, C. A., & Howell, J. M. (1991). Personal computing: toward a conceptual model of utilization. MIS Quarterly, 125–143. Vatanasakdakul, S., Aoun, C., Li, Y., & others. (2010). AIS in Australia: UTAUT application and cultural implication. 810

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Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478. Wan Ismail, W. N., & Mokhtar, M. Z. (2016). Application of TOE Framework in Examining the Factors Influencing Pre- and Post-Adoption Of CAS in Malaysian SMEs. International Journal of Information Technology and Business Management, 49(1), 26–37.

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