Digital banking usage intention questionnaire (UTAUT2)

Digital banking and mobile banking apps are a familiar topic for finance and banking students. UTAUT2 fits because it measures usefulness as well as habit, hedonic motivation and price value for individual users.

Research model

Model following UTAUT2 (Venkatesh et al., 2012): seven factors together affect behavioral intention.

• Performance Expectancy → Behavioral Intention (positive)
• Effort Expectancy → Behavioral Intention (positive)
• Social Influence → Behavioral Intention (positive)
• Facilitating Conditions → Behavioral Intention (positive)
• Hedonic Motivation → Behavioral Intention (positive)
• Price Value → Behavioral Intention (positive)
• Habit → Behavioral Intention (positive)

Questionnaire items

Original English wording. Answered on a 5-point Likert scale (1 = Strongly disagree … 5 = Strongly agree) unless the scale page says otherwise; (R) marks a reverse-coded item.

Performance Expectancy (PE) · UTAUT2 scale

Code	Item
PE1	I find [mobile Internet] useful in my daily life.
PE3	Using [mobile Internet] helps me accomplish things more quickly.
PE4	Using [mobile Internet] increases my productivity.

Source: Venkatesh et al. (2012). See UTAUT2 scale.

Effort Expectancy (EE) · UTAUT2 scale

Code	Item
EE1	Learning how to use [mobile Internet] is easy for me.
EE2	My interaction with [mobile Internet] is clear and understandable.
EE3	I find [mobile Internet] easy to use.
EE4	It is easy for me to become skillful at using [mobile Internet].

Source: Venkatesh et al. (2012). See UTAUT2 scale.

Social Influence (SI) · UTAUT2 scale

Code	Item
SI1	People who are important to me think that I should use [mobile Internet].
SI2	People who influence my behavior think that I should use [mobile Internet].
SI3	People whose opinions that I value prefer that I use [mobile Internet].

Source: Venkatesh et al. (2012). See UTAUT2 scale.

Facilitating Conditions (FC) · UTAUT2 scale

Code	Item
FC1	I have the resources necessary to use [mobile Internet].
FC2	I have the knowledge necessary to use [mobile Internet].
FC3	[mobile Internet] is compatible with other technologies I use.
FC4	I can get help from others when I have difficulties using [mobile Internet].

Source: Venkatesh et al. (2012). See UTAUT2 scale.

Hedonic Motivation (HM) · UTAUT2 scale

Code	Item
HM1	Using [mobile Internet] is fun.
HM2	Using [mobile Internet] is enjoyable.
HM3	Using [mobile Internet] is very entertaining.

Source: Venkatesh et al. (2012). See UTAUT2 scale.

Price Value (PV) · UTAUT2 scale

Code	Item
PV1	[mobile Internet] is reasonably priced.
PV2	[mobile Internet] is a good value for the money.
PV3	At the current price, [mobile Internet] provides a good value.

Source: Venkatesh et al. (2012). See UTAUT2 scale.

Habit (HT) · UTAUT2 scale

Code	Item
HT1	The use of [mobile Internet] has become a habit for me.
HT2	I am addicted to using [mobile Internet].
HT3	I must use [mobile Internet].

Source: Venkatesh et al. (2012). See UTAUT2 scale.

Behavioral Intention (BI) · UTAUT2 scale

Code	Item
BI1	I intend to continue using [mobile Internet] in the future.
BI2	I will always try to use [mobile Internet] in my daily life.
BI3	I plan to continue to use [mobile Internet] frequently.

Source: Venkatesh et al. (2012). See UTAUT2 scale.

Tips

• Price value (PV) and hedonic motivation (HM) are often weak when banking apps are free and utilitarian; if you drop them, explain this in Chapter 2.
• Behavioral intention receives 7 arrows: G*Power needs only 103 observations, but for PLS-SEM the inverse square root method requires 155, so the minimum sample is 155 and you should collect about 183 responses.
• The original PE4 is about work productivity, so it was rewritten for the personal finance context; the original HT3 'I must use' was rephrased so it is not read as compulsory.
• The original has moderators gender, age and experience; if you include them in the model, you need extra questions and a larger sample.

Work out the minimum sample size for this model; once the data are in, clean the responses and run the survey data analysis for your results chapter.

Studies that used this model

• Nguyen, Nguyen, Mai and Tran (2020). Determinants of Digital Banking Services in Vietnam: Applying UTAUT2 Model. Asian Economic and Financial Review, 10(6), 680–697. Sample: 241 respondents, mostly young with a university degree; UTAUT2 plus trust. Reliability: α 0.718-0.966 (Table 2).
• Pham, Pham, Thalassinos and Le (2022). The Application of SEM–Neural Network Method to Determine the Factors Affecting the Intention to Use Online Banking Services in Vietnam. Sustainability, 14(10), 6021. Sample: 433 customers in three economic regions; UTAUT2 adapted for online banking. Reliability: .
• Nguyen and Nguyen (2023). Analyzing the adoption of mobile banking service in Vietnam: Extending UTAUT2 with fear of COVID-19. Business Management, 33(3). Sample: Mobile banking users; UTAUT2 plus fear of COVID-19 (see the paper for the sample size). Reliability: .

Collecting responses

If your own network cannot reach the target respondents, you can pay for verified responses through Genius Data (referral link, we may earn a commission), paying only for approved responses.