Drivers of Mobile Learning App Usage—Measurement Model Inventory

Abstract

The Drivers of Mobile Learning App Usage—Measurement Model (Yeh et al., 2023) was developed to systematically investigate the complex psychological and behavioral factors that influence user acceptance of mobile learning (m-learning) applications. The study specifically focused on integrating core personality traits, learning readiness, and motivational drivers to create a comprehensive predictive model. The resulting instrument is a six-section questionnaire synthesized from several established measures, adapted and validated for the context of m-learning usage among the general population.

The measurement model incorporates ten focal constructs, drawing scales from existing literature: the Big Five Inventory (BFI) for personality (John et al., 1991), a modified 10-pair Internal-External (I-E) scale for Locus of Control (Carducci, 2009; derived from Rotter, 1966), and a specialized m-learning readiness scale (Lin et al., 2016). Motivational factors, including Intrinsic Motivation (perceived enjoyment) and Extrinsic Motivation (perceived usefulness), were conceptualized based on the framework proposed by Davis et al. (1992). The final component measures the user’s intention to utilize m-learning apps (Wang et al., 2009). Data collected from the general population were used to validate the measure, and comprehensive 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, Technology Acceptance Model.

Authors

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

Purpose

The primary aim of the Drivers of Mobile Learning App Usage—Measurement Model is to provide a robust psychometric tool capable of assessing the key psychological and behavioral factors influencing the adoption and continued use of mobile learning (m-learning) applications by mobile phone users.

The scale is designed specifically for predictive modeling in the technology acceptance domain, allowing researchers to quantify the relative impact of individual psychological characteristics (personality and locus of control) and context-specific factors (readiness and motivation) on the behavioral intention to use m-learning technology.

Construct

The measurement model is composed of ten distinct, yet interrelated, psychological and behavioral constructs designed to predict m-learning app usage intention. These constructs fall into three main categories: personality (Extraversion, Conscientiousness, Openness to Experience, Agreeableness, and Neuroticism, all derived from the Big Five Inventory), control orientation (Locus of Control), learning preparedness (Mobile Learning Readiness), and motivational drivers (Intrinsic Motivation and Extrinsic Motivation). The ultimate dependent variable measured is the intention to use m-learning apps.

The constructs measuring mobile learning readiness were conceptually based on three dimensions: m-learning self-efficacy, optimism regarding mobile technology, and self-directed learning abilities. Intrinsic motivation is measured through perceived enjoyment, while extrinsic motivation is assessed via perceived usefulness, following established models in the technology acceptance literature.

Validity

The validity of the measurement model was assessed using both Convergent Validity and Discriminant Validity techniques. For Convergent Validity, the Average Variance Extracted (AVE) was calculated for nine of the ten constructs, with values consistently exceeding the required threshold of 0.7. This high AVE indicates that the constructs successfully explained more than half of the variance in their respective indicator items.

Discriminant Validity was established using two primary methods. First, the cross-loadings of all items were verified to be lower on alternative constructs than on their intended construct. Second, the Fornell-Larcker method was applied, demonstrating that the square root of each construct’s AVE was greater than its correlation coefficients with all other constructs. These results collectively ensure that the constructs are empirically distinct and measure unique concepts within the model.

Reliability

Internal consistency reliability was rigorously evaluated using both Cronbach’s alpha and Composite Reliability (CR). The resulting values indicate high internal consistency across the latent variables. Cronbach’s alpha ranged from 0.87 to 0.95, while the Composite Reliability indices ranged from 0.90 to 0.97. These high reliability scores suggest that the items within each dimension measure the same underlying construct consistently, confirming the internal consistency reliability of the instrument.

Factor Analysis

All ten focal constructs were reflectively specified within the measurement model. Factor loadings, which serve as indicators of reliability and convergent validity, were utilized, requiring values greater than 0.7 to ensure that at least half of an item’s variance was extracted from its corresponding construct. An initial calculation led to the elimination of twelve items that did not meet this required threshold, including items measuring extraversion, conscientiousness, openness to experience, agreeableness, neuroticism, and mobile learning readiness.

After the removal of these weak indicators, a subsequent analysis revealed satisfactory factor loadings for all remaining items, supporting the structural integrity of the model. It is important to note that the Locus of Control construct was measured using a single item (a 10-pair forced-choice format based on Carducci, 2009), and therefore, standard validity or reliability information based on composite measures (like AVE or Cronbach’s alpha) was not reported for this specific item (Hair et al., 2017).

Instrument

Test Type: Original

Format: All measures, with the exception of the Locus of Control scale, are scored using 7-point Likert scales. For the Locus of Control measure, respondents choose one statement from each pair (Internal vs. External), with external control statements scored as 1 and internal control statements scored as 0, giving a score range of 0–10. Higher scores indicate greater external control.

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 were collected from the general population for scale validation.

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, Cronbach’s alpha.

Authors

• Author ORCID Identifier:

  • Yeh, Ching-Hsuan: http://orcid.org/0000-0003-1308-3602

  • Wang, Yu-Min: http://orcid.org/0000-0002-2096-6801

• Affiliation Email addresses:

  • 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

  • Wang, Yi-Shun (Email): [email protected]

• Correspondence Address:

  • Wang, Yi-Shun: [email protected]

Permissions & Fee and Test Year

• Permissions: Contact Publisher

• Commercial: No

• Fee: No

• Test Year: 2023

Reference’s

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 Inventory

The final validated measure consists of 48 items across ten constructs.

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.