Effect of high school students’ perception of accounting on their acceptance of using cloud accounting

ABSTRACT

This study explores the role of the perceived image of accounting in determining key variables that influence high school accounting students’ acceptance of using cloud accounting as a new technology. We adopted the technology acceptance model (TAM) as its theoretical framework. High school accounting students enrolled in the ‘Kyoto Subaru High School Career Course Business Game (KCB)’ at a mid-sized commercial high school in Japan were the participants. During the KCB, the students engaged in learning interventions using business games that incorporated cloud accounting. We found that high school students’ image of accounting as a decision-making tool is important to strengthen the perceived usefulness of cloud accounting on their intention to use its technology. However, we could not demonstrate that students’ accounting perceptions moderate another significant TAM path between perceived ease of cloud accounting use and intention of its technology use. These results provide new insights regarding effective and relevant ways to offer students more opportunities to use the new technology and gain hands-on experiences in the accounting education curriculum.

KEYWORDS:

• Cloud accounting
• technology acceptance model
• perception of accounting
• accounting education
• digital economy

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 The period of integrated study is prescribed in the Japanese Government Policies in Education, Culture, Sports, Science and Technology 2001, issued by the Ministry of Education, Culture, Sports, Science, and Technology of Japan (MEXT). Schools are required to offer the semester-based course at the elementary, junior, and high school (primary and secondary school) levels. This course aims to foster students’ ability to make connections, think, judge, and solve problems on their own. It also enables them to think about their own lives, urges them to explore subjects with creativity and subjectivity, and to solve problems through their own ways of learning and thinking. (https://www.mext.go.jp/b_menu/hakusho/html/hpac200101/hpac200101_2_034.html)

2 Yenbo-kaikei^® is a trademark of Yenbo Internet Service Co., which does not sponsor, authorize, or endorse this manuscript.

3 Approval for using human subjects was obtained from the relevant authority of the institution where the experiment was conducted.

4 Two different classes were taught by the same group of five teachers.

5 We believed participating students clearly understood our intention to avoid a ‘neutral’ response and accurately answered this question because they have studied accounting and bookkeeping since they started learning at high school. Our participants were third year students; therefore, they had enough time (two and a half years) and learning experiences to form their opinion about this question.

6 A preliminary check was also conducted to investigate similarities between Classes 1 and 2 in the three key variables of the TAM (perceived usefulness of cloud accounting, perceive ease of cloud accounting use, and behavioral intention to use cloud accounting) and students’ perception of accounting. Descriptive statistics of these variables and analysis results are shown in Table 1. T-test results indicated that there were no significant differences between the two classes (t[57]    1.152, p = 0.254 for perceived usefulness of cloud accounting; t[57]    0.860, p = 0.349 for perceive ease of cloud accounting use; t[57]    0.633, p = 0.530 for intention to learn cloud accounting; t[57]    1.118, p = 0.268 for students’ perception of accounting).

7 Bootstrapping methods, which were originally developed by Bradley Efron, are computer techniques that allow resampling of a large number of small samples with replacement from the original sample to provide an estimate of the standard error and generate a confidence interval (Efron, 1979; Hayes, 2009). According to B. Efron, even setting the number of samples at 50 is likely to lead to fairly good standard error estimates (Efron et al., 2004).

Additional information

Funding

This work was supported by JSPS KAKENHI: [grant no 21K01816].