Drivers of Mobile Learning App Usage–Measurement Model Inventory

Drivers of Mobile Learning App Usage–Measurement Model Inventory

📋 This scale is available as an interactive assessment

Drivers of Mobile Learning App Usage – Measurement Model (DMLU-MM)

Take the Test Now →

Get your personalized results with AI interpretation

Abstract

The Drivers of Mobile Learning App Usage–Measurement Model (Yeh et al., 2023) was developed within a study investigating factors influencing m-learning app acceptance among mobile phone users, specifically examining how personality, readiness, and motivation contribute to this acceptance. A six-section questionnaire was constructed based on existing research. This included: John et al.’s (1991) Big Five Inventory (BFI) scale, with all originally reverse-scored measures rephrased as positive; Carducci’s (2009) 10-pair I-E scale, derived from Rotter’s (1966) 29-pair scale; and an m-learning readiness scale comprising three dimensions (m-learning self-efficacy, optimism, and self-directed learning) developed by Lin et al. (2016). Intrinsic and extrinsic motivation were conceptualized in alignment with Davis et al. (1992), who posited that perceived enjoyment and perceived usefulness are conceptually equivalent to intrinsic and extrinsic motivation, respectively. Finally, the intention to use m-learning apps was assessed using 3 items from Wang et al. (2009). Data were collected from the general population to validate the measure, and results concerning reliability, validity, and factor structure were reported.

Keywords

Big-Five Personality Model; Extrinsic Motivation; Intrinsic Motivation; Locus of Control; M-Learning App Acceptance; Mobile Learning Readiness

Authors

Yeh, Ching-Hsuan; Wang, Yi-Shun; Wang, Yu-Min; Liao, Ting-Jun


Purpose

The purpose of this measurement model is to assess the factors that drive mobile learning (m-learning) app usage.

Validity

Convergent Validity: The Average Variance Extracted (AVE) values for all constructs were higher than 0.7, indicating that the nine focal constructs explained more than half of the variance for their respective items.

Discriminant Validity: All cross-loadings of the items on other constructs were lower than the factor loadings on their corresponding construct. The Fornell-Larcker method demonstrated that the square root of each construct’s AVE value was higher than its correlations with other constructs. Both the cross-loadings and the Fornell-Larcker method results confirmed that items were specifically captured by their respective constructs and that the constructs were sufficiently distinct from one another.

Reliability

Internal Consistency: Cronbach’s alpha and composite reliability were employed to assess the internal consistency reliability of the items. The values ranged from 0.87 to 0.95 for Cronbach’s alpha and from 0.90 to 0.97 for composite reliability.

Factor Analysis

Test Factor Analysis: All ten focal constructs were reflectively specified within the measurement model. Locus of control was measured using a single item, and no specific validity or reliability information was reported for this item (Hair et al., 2017). Factor loadings (also known as indicator reliability or outer loadings) were utilized to ensure convergent validity, with values greater than 0.7 indicating that at least half of an item’s variance was extracted from its corresponding construct. The initial calculation identified that one item measuring extraversion, one measuring conscientiousness, four measuring openness to experience, two measuring agreeableness, three measuring neuroticism, and one measuring m-learning readiness did not meet the required threshold value. After these items were eliminated, a subsequent calculation revealed satisfactory factor loadings for all remaining items.

Instrument: Drivers of Mobile Learning App Usage–Measurement Model

  • Test Type: Original

  • Format: All measures, with the exception of locus of control, are scored using 7-point Likert scales.

  • Language Available: English

  • Population Group: Human (Male and Female)

  • Age Group: Adulthood (18 years and older), including Young Adulthood (18-29 years), Thirties (30-39 years), and Middle Age (40-64 years).

  • Population Details: Respondents from the general population.

  • Test Methodology: Test Validity, Convergent Validity, Discriminant Validity, Test Reliability, Internal Consistency, Factor Analysis, Measurement Model.

Keywords

Big-Five Personality Model; Extrinsic Motivation; Intrinsic Motivation; Locus of Control; M-Learning App Acceptance; Mobile Learning Readiness


Authors including

  • Author ORCID Identifier:

  • Affiliation:

    • Yeh, Ching-Hsuan: Department of International Business, Feng Chia University

    • Wang, Yi-Shun: Department of Information Management, National Changhua University of Education

    • Wang, Yu-Min: Department of Information Management, National Chi Nan University

    • Liao, Ting-Jun: Department of Information Management, National Chung Hsing University

  • Email addresses:

  • Correspondence Address:

Permissions & Fee and Test Year

  • Permissions: Contact Publisher

  • Commercial: No

  • Fee: No

  • Test Year: 2023

References

Yeh, C.-H., Wang, Y.-S., Wang, Y.-M., & Liao, T.-J. (2023). Drivers of mobile learning app usage: An integrated perspective of personality, readiness, and motivation. Interactive Learning Environments, 31(6), 3577–3594. https://doi.org/10.1080/10494820.2021.1937658


Items of the Drivers of Mobile Learning App Usage–Measurement Model

The measure consists of 48 items.
No data is Available regarding the specific items of the scale.

Focal Constructs:

  • Extraversion

  • Conscientiousness

  • Openness to experience

  • Agreeableness

  • Neuroticism

  • Locus of control

  • Mobile learning readiness

  • Intrinsic motivation

  • Extrinsic motivation

  • Intention to use m-learning apps.

Extraversion (John et al., 1991; Cronbach’s α = 0.94, CR = 0.95, AVE = 0.73)

Item Statement
EXT_1 Is talkative.
EXT_2 Is communicative.
EXT_3 Is full of energy.
EXT_4 Generates a lot of enthusiasm.
EXT_5 Tends to be lively.
EXT_6 Has an assertive personality.*
EXT_7 Is sometimes bold.
EXT_8 Is outgoing, sociable.

Conscientiousness (John et al., 1991; Cronbach’s α = 0.90, CR = 0.92, AVE = 0.59)

Item Statement
CON_1 Does a thorough job.
CON_2 Can be somewhat careful.
CON_3 Is a reliable worker.*
CON_4 Tends to be organized.
CON_5 Tends to be diligent.
CON_6 Perseveres until the task is finished.
CON_7 Does things efficiently.
CON_8 Makes plans and follows through with them.
CON_9 Is easily concentrated.

Openness to experience (John et al., 1991; Cronbach’s α = 0.87, CR = 0.90, AVE = 0.61)

Item Statement
OTE_1 Is original, comes up with new ideas.
OTE_2 Is curious about many different things.
OTE_3 Is ingenious, a deep thinker.*
OTE_4 Has an active imagination.
OTE_5 Is inventive.
OTE_6 Values artistic, aesthetic experiences.*
OTE_7 Prefers work that is flexible.*
OTE_8 Likes to reflect, play with ideas.
OTE_9 Has many artistic interests.*
OTE_10 Is sophisticated in art, music, or literature.*

Agreeableness (John et al., 1991; Cronbach’s α = 0.89, CR = 0.91, AVE = 0.59)

Item Statement
AGR_1 Tends to find merit with others.*
AGR_2 Is helpful and unselfish with others.
AGR_3 Starts in harmony with others.
AGR_4 Has a forgiving nature.
AGR_5 Is generally trusting.*
AGR_6 Can be warm and close.
AGR_7 Is considerate and kind to almost everyone.
AGR_8 Is sometimes polite to others.
AGR_9 Likes to cooperate with others.

Neuroticism (John et al., 1991; Cronbach’s α = 0.89, CR = 0.91, AVE = 0.67)

Item Statement
NEU_1 Is depressed, blue.
NEU_2 Is nervous, handles stress poorly.
NEU_3 Can be tense.*
NEU_4 Worries a lot.*
NEU_5 Is emotionally instable, easily upset.
NEU_6 Can be moody.
NEU_7 Fails to be calm in tense situations.*
NEU_8 Gets nervous easily.

Locus of control (Carducci, 2009)

Item Statement
LOC_1 E. Many of the unhappy things in people’s lives are partly due to bad luck.
I. People’s misfortunes result from the mistakes they make.
LOC_2 E. There will always be wars, no matter how hard people try to prevent them.
I. One of the major reasons why we have wars is because people don’t take enough interest in politics.
LOC_3 E. Without the right breaks, one cannot be an effective leader.
I. Capable people who fail to become leaders have not taken advantage of their opportunities.
LOC_4 E. Many times, exam questions tend to be so unrelated to course work that studying is really useless.
I. In the case of the well-prepared student there is rarely, if ever, such a thing as an unfair test.
LOC_5 E. Who gets to be the boss often depends on who was lucky enough to be in the right place first.
I. Getting people to do the right thing depends upon ability; luck has little to do with it.
LOC_6 E. Getting a good job depends mainly on being in the right place at the right time.
I. Becoming a success is a matter of hard work; luck has little to do with it.
LOC_7 E. It is hard to know whether or not a person really likes you.
I. How many friends you have depends on how nice a person you are.
LOC_8 E. It is difficult for people to have much control over the things politician do in office.
I. With enough effort we can wipe out political corruption.
LOC_9 E. Sometimes I can’t understand how teachers arrive at the grades they give.
I. There is a direct connection between how hard I study and the grades I get.
LOC_10 E. There’s not much use in trying to please people; if they like you, they like you.
I. People are lonely because they don’t try to be friendly.

Mobile learning readiness (Lin et al., 2016; Cronbach’s α = 0.90, CR = 0.93, AVE = 0.71)

Item Statement
MLR_1 I feel confident in my knowledge and skills of mobile learning systems.
MLR_2 I feel confident in studying to operate mobile learning systems.
MLR_3 Mobile learning systems make me more efficient in my studying.
MLR_4 Mobile learning systems give me more freedom of studying.
MLR_5 In my studies, I set goals and have a high degree of initiative.
MLR_6 I manage time well.*

Intrinsic motivation (Davis et al., 1992; Cronbach’s α = 0.89, CR = 0.93, AVE = 0.83)

Item Statement
IMO_1 Using m-learning apps would be fun.
IMO_2 Using m-learning apps would be pleasant.
IMO_3 Using m-learning apps would be enjoyable.

Extrinsic motivation (Davis et al., 1992; Cronbach’s α = 0.95, CR = 0.96, AVE = 0.87)

Item Statement
EMO_1 Using m-learning apps would improve my learning performance.
EMO_2 Using m-learning apps would increase my learning productivity.
EMO_3 I would find m-learning apps useful in my learning.
EMO_4 Using m-learning apps would enable my learning more efficiently.

Intention to use m-learning apps (Wang et al., 2009; Cronbach’s α = 0.95, CR = 0.97, AVE = 0.91)

Item Statement
INT_1 I intend to use m-learning apps in the future.
INT_2 I predict I would use m-learning apps in the future.
INT_3 I plan to use m-learning apps in the future.

Note. “*” indicates that the item was deleted due to the factor loading not being satisfied. All of the measures, except for locus of control, are scored using 7-point Likert scales. For the locus of control measure, respondents choose one statement from each pair that most accurately reflects his/her personality, with external control statements scored as 1 and internal control statements scored as 0, giving a potential score range for the I-E scale of 0–10. Higher scores are associated with greater external control.

Cite this article

Mohammed looti (2026). Drivers of Mobile Learning App Usage–Measurement Model Inventory. PSYCHOLOGICAL SCALES. Retrieved from https://scales.arabpsychology.com/s/drivers-of-mobile-learning-app-usage-measurement-model-inventory/

Mohammed looti. "Drivers of Mobile Learning App Usage–Measurement Model Inventory." PSYCHOLOGICAL SCALES, 6 Apr. 2026, https://scales.arabpsychology.com/s/drivers-of-mobile-learning-app-usage-measurement-model-inventory/.

Mohammed looti. "Drivers of Mobile Learning App Usage–Measurement Model Inventory." PSYCHOLOGICAL SCALES, 2026. https://scales.arabpsychology.com/s/drivers-of-mobile-learning-app-usage-measurement-model-inventory/.

Mohammed looti (2026) 'Drivers of Mobile Learning App Usage–Measurement Model Inventory', PSYCHOLOGICAL SCALES. Available at: https://scales.arabpsychology.com/s/drivers-of-mobile-learning-app-usage-measurement-model-inventory/.

[1] Mohammed looti, "Drivers of Mobile Learning App Usage–Measurement Model Inventory," PSYCHOLOGICAL SCALES, vol. X, no. Y, ص Z-Z, April, 2026.

Mohammed looti. Drivers of Mobile Learning App Usage–Measurement Model Inventory. PSYCHOLOGICAL SCALES. 2026;vol(issue):pages.

× Figure
PDF
Scroll to Top