The Intention of Accounting Software’s Adoption for Village-owned

Enterprises Financial Reporting in Indonesia

Rini Indahwati

and Nurlinda

State Polytechnic of Medan, Jl. Almamater no 1 Kampus USU, Medan, Indonesia

Department of Accounting, Jl. Almamater No.1 Kampus USU Medan, Indonesia

Keywords: Intention, Voe, Accounting Software

Abstract: This research aims to analyse the influence of performance expectancy, effort expectancy, social influence

and facilitating conditions on the intention to adopt accounting software in village-owned enterprises

(VOE). The data collected by a convenient sampling method by sending the questionnaire to the

respondents by e-form. The result showed that only performance expectancy significantly affected the

intention to adopt accounting software for financial reporting. Meanwhile, effort expectancy, social

influence and facilitating conditions were not significantly affected the intention to adopt accounting

software in village-owned enterprises.

1 INTRODUCTION

Village-owned enterprises (VOE) are the platform

for village’s economics development. The existence

of these enterprises will raise the level of people’s

wealthy in villages. This is the reason of the

importance of village-owned enterprises in

Indonesia.

Recent situation show that villages receipt the

donation from the Ministry of Village, Development

of Disadvantaged Regions and Transmigration for

the village’s development. The data show ed that the

reporting of the budget still inappropriate. For

example, in Parigi Moutong District that received

the donation from Ministry of Village, Development

of Disadvantaged Regions and Transmigration, but

when the auditor examines the reporting of the

donation, the auditors found that there are several

problems with the financial reporting

(https://paluekspres.fajar.co.id). This problem

related with the capability of the human resources to

perform financial report.

Technology, especially accounting software,

could help this financial reporting’s problem.

UTAUT is a model that used to explained the

behaviour of technology’s users (V Venkatesh,

Morris, GB, & Davis, 2003). This model consist of 8

(eight) variables to predict the intention of

technology’s adoption. The variables are

behavioural intention, use behaviour, performance

expectancy, effort expectancy, social influence and

facilitating conditions.

2 LITERATURE REVIEW

2.1 UTAUT Model

The Unified of Acceptance and Use of Technology

(UTAUT) developed by V Venkatesh et al.,

2003).UTAUT Model combined the 8 (eight) main

variables; theory of reasoned action (TRA),

technology acceptance model (TAM), motivational

model (MM), theory of planned behaviour (TPB),

combined TAM and TPB, Model of PC utilization

(MPTU), innovation diffusion theory (IDT), and

social cognitive theory (SCT).

UTAUT aims to help entities to understand the

users’ reaction to the new technology. (Wang,

2005). UTAUT developed from TAM’s Model that

consist of 4 (four) constructs that affected the

intention to use the new technology; performance

expectancy, effort expectancy, social influence and

facilitating conditions (V Venkatesh et al., 2003).

386

Indahwati, R. and Nurlinda, .

The Intention of Accounting Software’s Adoption for Village-owned Enterprises Financial Reporting in Indonesia.

DOI: 10.5220/0009205703860391

In Proceedings of the 2nd Economics and Business International Conference (EBIC 2019) - Economics and Business in Industrial Revolution 4.0, pages 386-391

ISBN: 978-989-758-498-5

2.2 Performance Expectancy

Performance expectancy defined as the level of

someone’s perception that using a technology will

help themselves to gain the highest performance (V

Venkatesh et al., 2003). According to Handayani &

Sudiana (2015), indicators that used to measure

performance expectancy are complexity, perception

of easiness of use and easiness to learn.

Meanwhile, other indicators that used to

measure the performance indicators are (Hormati,

2012)

1. Increasing the productivity

2. Increasing the quality

3. Increasing the effectiveness

2.3 Effort Expectancy

Effort expectancy refers to the level of the easiness

of using a new technology (V Venkatesh et al.,

2003). The easier a system to operate then user need

less effort to perform. A friendly system asked for

less effort.

According to Handayani & Sudiana (2015),

several indicators that operated to measure effort

expectancy are:

1. The Management easiness’s perception

2. Speed for doing a job

3. Performance’s gain

4. Motivation

Hormati (2012) operated these indicators to

measure effort expectancy:

1. Easy to learn

2. Easy to use

3. Interaction

4. Easy to perform a job

2.4 Social Influence

Social influence defined as the level of individual’s

perception of others influences in using a new

technology (V Venkatesh et al., 2003). According to

Handayani & Sudiana (2015), indicators that used in

measuring the social influence on information

systems are:

1. Family factor

2. Friends

3. Social Factors

4. Influencing people

Hormati (2012) operated these indicators to

measure the social influence variable:

1. The influence of colleagues

2. The influence of Manager (Leader)

3. Company’s support

4. Social Status

2.5 Facilitating Condition

Facilitating condition defined as someone’s belief

about the infrastructure that will support the use of

new technology (V Venkatesh et al., 2003).

According to Handayani & Sudiana (2015),

indicators that used to measure the facilitating

condition are:

1. Condition that will facilitate

2. Knowledge

3. Compatibility

4. Acceptable

5. The influence of co-worker

Hormati (2012), used these indicators to

measure facilitating condition variable:

1. Availability of facilities

2. User’s skill

3. Compatibility

4. Availability of experts

2.6 Intention for Adoption

Intention defined as the willingness to perform a

behaviour (Hormati, 2012). Behaviour explained as

the interest to do something, meanwhile intention

will determine the behaviour (Hartono, 2007).

Handayani & Sudiana (2015) used these

indicators to measure the intention for technology

adoption:

1. Intent to use the technology more often

2. Predicted to use the technology

3. Plan to use the new technology

4. Confidence to use the new technology

(Hormati, 2012) operated 3 (three) indicators to

measure the intention for technology adoption:

1. Willing to use

2. Predicting to use

3. Planning to use

2.7 The Effect of Performance

Expectancy on the Intention for

Adoption

Hormati (2012) found that performance expectancy

affected the intention to use in Indonesia’s

government. Indahwati & Afiah (2014) also found

the performance expectancy affected the intention to

use accounting software for Small Medium

Enterprises.

Handayani & Sudiana found that performance

expectancy affected the intention to use. Lai, Lai, &

Jordan (2009) found the same result. Im, Hong &

The Intention of Accounting Software’s Adoption for Village-owned Enterprises Financial Reporting in Indonesia

387

Kang (2011) showed the same result also. V

Venkatesh, Thong, & Xu (2012) found that there

was a significant effect of performance expectancy

on the intention to use new technology.

According to those researches above, so the

hypothesis of this research is

HI: There is a positive significant effect of

performance expectancy on the intention to use

accounting software in VOE

2.8 The Effect of Effort Expectancy on

the Intention for Adoption

Several researchers found that there is an effect of

effort expectancy on intention to use new technology

[Hormati (2012); Indahwati & Afiah (2014); V

Venkatesh, Thong, & Xu (2012); Lai et al (2009)].

Different result found by Handayani & Sudiana

(2015), which is the result was effort expectancy not

significantly affected the intention to use new

technology.

Ling (2008) concluded that effort expectancy

played an important role to affect the intention to use

ERP. Payne & Curtis (2008) found that effort

expectancy is a significant variable in the intention

to use audit technology.

Im et al (2011) found that effort expectancy had

a positive significant effect on the intention to adopt

new technology

According to the researches’ result above, so the

hypothesis of this research is:

H2: There is a positive significant effect of effort

expectancy on the intention to use accounting

software for VOE

2.9 The Effect of Social Influence on

the Intention for Adoption

The result of Hormati (2012), Indahwati & Afiah

(2014) found that social influence affected the

intention to adopt new technology. Handayani &

Sudiana (2015) found that social influence affected

the intention behaviour.

Lai, Lai, & Jordan (2009) also found that social

influence had ad positive significant effect on the

intention to adopt new technology. V Venkatesh,

Thong, & Xu (2012) found that there was a

significant effect of social influence on the intention

behaviour.

Based on the researchers above, so the

hypothesis of this research is:

H3: There is a positive significant effect of

social influence on the intention to use accounting

software for VOE

2.10 The Effect of Facilitating Condition

on the Intention for Adoption

Based on several researches, it can be stated that

facilitating conditions had a significant effect on the

intention to adopt new technology [Hormati (2012);

Indahwati & Afiah (2014)]. (Oswari, Suhendra, &

Harmoni, 2008) found that facilitating conditions

significantly affected the performance of small-

medium enterprises.

Payne & Curtis (2008) concluded that

facilitating conditions was a significant factor for the

intention to adopt audit technology. Meanwhile, Im

et al (2011) found that facilitating conditions had a

significant impact on the intention to use new

technology. Venkatesh (2012) found that intention to

use and facilitating conditions had a significant

relationship.

According to several researches above, in can be

stated that:

H4: There is a positive significant effect of

facilitating conditions on the intention to use

accounting software for VOE

3 METHOD

3.1 Measurement’s Parameter

3.1.1 Performance Expectancy (X1)

Performance expectancy defined as the level of

someone’s perception that using a technology will

help themselves to gain the highest performance (V

Venkatesh et al., 2003). Indicators that used for

measuring the performance expectancy, refers to

Hormati (2012) consist of increasing the

productivity, increasing the quality , increasing the

effectiveness. Variable measure with the Likert scale

1 – 5.

3.1.2 Effort Expectancy (X2)

Effort expectancy refers to the level of the easiness

of using a new technology (V Venkatesh et al.,

2003). Indicators that used for measuring the

performance expectancy, refers to Hormati (2012)

consist of easy to learn, easy to use, interaction, easy

to perform a job.

EBIC 2019 - Economics and Business International Conference 2019

388

3.1.3 Social Influence (X3)

Social influence defined as the level of individual’s

perception of others influences in using a new

technology (V Venkatesh et al., 2003). Indicators

that used for measuring the social influence refers to

Hormati (2012) consist of the influence of

colleagues, the influence of manager (leader) ,

company’s support, social status . Variable

measured by Likert Scale 1 – 5.

3.1.4 Facilitating Conditions

Facilitating condition defined as someone’s belief

about the infrastructure that will support the use of

new technology (V Venkatesh et al., 2003). Hormati

(2012), used these indicators to measure facilitating

condition variable : availability of facilities, user’s

skill, compatibility, availability of experts.

3.1.5 Intention to use (Y)

Intention defined as the willingness to perform a

behaviour (Hormati, 2012). Behaviour explained as

the interest to do something, meanwhile intention

will determine the behaviour (Hartono, 2007).

(Hormati, 2012) operated 3 (three) indicators to

measure the intention for technology adoption:

willing to use, predicting to use , planning to use

3.2 Research Model

Figure 1.

3.3 Population and Sample

The population of this research are all the village-

owned enterprises (VOE) that joint the village-

owned enterprise forum in 2019. Total VOE that

listed as the member of village-owned enterprise

forum in 2019 are 510 enterprises.

Samples chose by the Slovin formula. Based on

that formula, total samples that had been chosen are

84 (eighty four) VOE.

3.4 Instrument & Data Collecting

The instrument to measure the research data is a

questionnaire. Research instrument operated with

the Likert Scale form 1- 5.

3.5 Data Analysis

Data analysed with PLS (Partial Least Square).

Before the analysis process, we examined the

validity and reliability of the data.

Table 1.

4 RESULTS AND DISCUSSION

4.1 Descriptive Analysis

Based on table above, we can figure out that most of

the respondents are men. Most of the respondents

are graduated from college, and aged between 35 –

44 years old. Most of them are able to uses computer

and also had a proper financial skill.

The Intention of Accounting Software’s Adoption for Village-owned Enterprises Financial Reporting in Indonesia

389

4.2 Data Analysis

Figure 2.

The figure above, showed that X1 formed by 4

(four) indicators, which are helping the financial

reporting process, helping in accelerated the time for

financial reporting, enhancing the user productivity

and providing the chance for the improvement of

financial reporting process. X2 formed by 2 (two)

indicators. The indicators are the easiness to use and

the application was easy to use.

X3 formed by 2 (two) indicators, which are

influence from people who was respected by user,

apparatus’s influence to user for adopting

accounting software. X4 formed by 3 (three)

indicators. The indicators are facilities in VOE, the

knowledge to use the facilities, compatibility of

facilities with other’s facilities in VOE.

Table 2.

Based on the table above, it can be shown that

only X1 (performance expectancy) significantly

affected the intention to adopt accounting software

(Y). The p-value for performance expectancy to

intention to adopt is 0.033< 0.05.

Meanwhile, the p-value for X2 (effort

expectancy) to Y (intention to adopt) is 0.470> 0.05.

It means that effort expectancy not significantly

affected the intention to adopt accounting software.

P-value for X3 (social influence) to Y is 0.346 >

0.05. This value means that social influence not

significantly affected the intention to use in village-

owned enterprises.

Furthermore, the p-value for X4 (facilitating

conditions) to Y is 0.326. It means that facilitating

conditions not significantly affected the intention to

use accounting software for financial reporting in

VOE.

5 CONCLUSIONS

Based on the discussion, it can be concluded that

performance expectancy significantly affected the

intention to use accounting software for financial

reporting. Effort expectancy, social influence,

facilitating conditions are not significantly affected

the intention to adopt accounting software for

financial reporting.

ACKNOWLEDGEMENTS

The authors gratefully acknowledge that the present

research is supported by Politeknik Negeri Medan,

Accounting Department of Politeknik Negeri

Medan.

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