Determinants of Donation Intention in Live Streaming–Model Inventory

Abstract

The Determinants of Donation Intention in Live Streaming–Model (Chou & Nguyen, 2023) is a comprehensive psychometric instrument designed to investigate the complex factors that influence a user’s intent to provide financial donations during live streaming sessions. The scale development is theoretically grounded in the dedication-constraint framework, which examines how the perceived quality of relationships and interactions within the live streaming environment drives subsequent post-adoption behaviors, such as monetary contributions.

The measure comprises items adapted from pre-existing research, which were translated into Chinese by a Taiwanese bilingual expert. To ensure strict conceptual and linguistic equivalence, a rigorous back-translation technique (Schaffer & Riordan, 2003) was implemented. Following minor contextual adjustments to align the wording with the live streaming setting, the questionnaire was validated via a pilot test involving experienced users. The final measure was administered to a sample of experienced live streaming users in Taiwan, and its psychometric integrity was confirmed using Structural Equation Modeling (SEM), yielding favorable results for both reliability and validity.

Keywords

Behavioral Compulsion, Cognitive Preoccupation, Dedication-Constraint Framework, Donation Intention, Entertainment, Live Streaming Interactions, Personalization, Responsiveness, Social Feedback, Structural Equation Modeling.

Authors

Chou, Shih-Wei, Nguyen, Trieu.

Purpose

The principal objective of this measurement model is to systematically assess and quantify the various psychological and interactional factors that serve as determinants of donation intention within the context of live streaming platforms. The scale provides researchers with a robust tool for mapping the antecedents of financial giving to streamers.

This inventory is specifically useful for understanding how relationship quality factors (such as responsiveness and personalization) and constraints (such as cognitive preoccupation) interact to predict a user’s likelihood of engaging in donation behavior.

Construct

The inventory consists of 29 items measuring eight distinct constructs related to the user experience and psychological engagement on live streaming platforms. The constructs measured are: Personalization, Responsiveness, Entertainment, Affective Feedback, Social Feedback, Cognitive Preoccupation, Behavioral Compulsion, and Donation Intention.

These constructs capture the essence of the dedication-constraint approach, where dedication to the platform (e.g., through positive feedback and entertainment value) and behavioral constraints (e.g., compulsive thoughts or behaviors) influence the ultimate post-adoption outcome of donation intention.

Validity

The results demonstrated strong psychometric validity, specifically confirming both Convergent Validity and Discriminant Validity. For convergent validity, all factor loadings were significant and exceeded the 0.7 threshold. Composite Reliability (CR) values ranged favorably from 0.877 to 0.929. The square roots of the Average Variance Extracted (AVE), ranging from 0.829 to 0.892, were consistently greater than all correlations among the constructs, satisfying the Fornell & Larcker (1981) criterion.

Regarding discriminant validity, the study found that the loading of each measurement item on its assigned construct was greater than its loading on any other construct (Hair Jr et al., 2021). Furthermore, all Heterotrait-Monotrait Ratio of Correlations (HTMT) values were below the conservative cut-off value of 0.85 (Henseler et al., 2015), providing robust confirmation that the constructs are empirically distinct.

Reliability

The scale exhibited high levels of internal consistency, confirming its reliability. The calculated Cronbach’s alpha values for all constructs surpassed the standard acceptance threshold of 0.70 (Hair Jr et al., 2021). Additionally, the Composite Reliability (CR) values, which ranged from 0.877 to 0.929, further indicated excellent internal consistency among the items designed to measure each latent variable.

Factor Analysis

To address potential measurement issues, Harman’s single-factor test was utilized to detect the presence of common method bias (CMB). The analysis revealed that the variance accounted for by the first factor constituted only 41.15% of the total variance. Since this result is below the critical benchmark of 50% (Podsakoff et al., 2012), the study concluded that common method bias was not a significant threat to the validity of the measurement model.

Instrument

Test Type: Original Inventory/Questionnaire

Format: The measurement items are evaluated using a seven-point Likert scale, ranging from 1 (completely disagree) to 7 (completely agree). The administration method was electronic.

Language Available: Chinese

Population Group: Human; Male; Female

Age Group: Adulthood (18 yrs & older); Young Adulthood (18-29 yrs); Thirties (30-39 yrs)

Population Details:

• Location: Taiwan

• Respondents: Experienced Live Stream Users

Test Methodology: Test Validity; Convergent Validity; Discriminant Validity; Test Reliability; Internal Consistency; Measurement Model; Structural Equation Modeling

Authors

Author ORCID Identifier:

Nguyen, Trieu: http://orcid.org/0000-0003-1114-4715

Affiliation Email addresses:

Chou, Shih-Wei: National Kaohsiung University of Science and Technology Department of Information Management ([email protected])

Nguyen, Trieu: National Kaohsiung University of Science and Technology College of Management, IMBA Program ([email protected])

Correspondence Address:

Nguyen, Trieu: No. 1, University Road, Yanchao District, Kaohsiung City, Taiwan, Province of China, [email protected]

Permissions & Fee and Test Year

Permissions: May use for Research/Teaching

Commercial: No

Fee: No

Test Year: 2023

References

Chou, S.-W., & Nguyen, T. (2023). Understanding donation intention in live streaming: A dedication-constraint approach. Computers in Human Behavior, 144, 1–11. https://doi.org/10.1016/j.chb.2023.107757

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.

Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). SAGE publications.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.

Podsakoff, P. M., MacKenzie, S. B., & Podsakoff, N. P. (2012). Sources of method bias in social science research and recommendations on how to control it. Annual Review of Psychology, 63, 539–569.

Schaffer, B. S., & Riordan, C. M. (2003). A review of cross-cultural methodologies for organizational research: A best-practices approach. Organizational Research Methods, 6(2), 169–215.

Items of the Determinants of Donation Intention in Live Streaming–Model Inventory

IMPORTANT: The following scale items must be preserved in their original language and must not be changed in any way.

This measure includes 29 items. The constructs measured are: Personalization; Responsiveness; Entertainment; Affective Feedback; Social Feedback; Cognitive Preoccupation; Behavioral Compulsion; Donation Intention.

Personalization (Adapted from Komiak and Benbasat, 2006)

• PE1. The streamers offer professional content that suits my personal interests or concerns.

• PE2. The streamers can focus on my interests and concerns.

• PE3. The streamers provide personalized content or information for my personal needs.

Responsiveness (Adapted from Xue et al., 2020)

• RE1. The streamers can answer my questions and requests in time.

• RE2. The response of the streamers is closely related to my problems and requests.

• RE3. The streamers can provide relevant information for my inquiry in time.

Entertainment (Adapted from Xue et al., 2020)

• EN1. The streamers regularly initiate interesting moments to attract me to interact with them.

• EN2. The streamers share interesting topics with fans in an interesting way, including their amusing shared opinions.

• EN3. Participating in funny social activities about my interested topic with the streamers can make me enjoy the delight of live streams.

Affective feedback (Adapted from Lin et al., 2008)

• AF1. It feels satisfying to interact with streamers and other viewers in live streams.

• AF2. It feels good to interact with streamers and other viewers in live streams.

• AF3. It is fun to interact with streamers and other viewers in live streams.

• AF4. It is enjoyable to interact with streamers and other viewers in live streams.

Social feedback (Adapted from Hassan et al., 2019)

• SF1. When streamers and other viewers in live streams like and respond to my interactions, I like it.

• SF2. When streamers and other viewers in live streams notice my interaction, I like it.

• SF3. When my interactions in live streams are noticed, I feel good.

• SF4. I often pay attention to interactions that other viewers have in live streams.

Cognitive preoccupation (Adapted from Haagsma et al., 2013)

• CP1. Sometimes I cannot stop thinking about interacting with streamers and other viewers in live streams.

• CP2. There are images of interacting with streamers and other viewers in live streams that come into my mind that I cannot erase.

• CP3. There are thoughts of interacting with streamers and other viewers in live streams that keep entering my head.

• CP4. My thoughts frequently return to the idea of interacting with streamers and other viewers in live streams.

Behavioral compulsion (Adapted from Haagsma et al., 2013)

• BC1. I find it difficult to overrule my impulse to interact with streamers and other viewers in live streams.

• BC2. I find it difficult to overcome my tendency to interact with streamers and other viewers in live streams.

• BC3. It would be difficult for me to control my tendency to interact with streamers and other viewers in live streams.

• BC4. It is hard to restrain my urge to interact with streamers and other viewers in live streams.

Donation Intention (Adapted from Ye et al., 2015)

• DI1. I am willing to make a donation to streamers.

• DI2. I intend on making a donation to streamers.

• DI3. I am very likely to make a donation to streamers.

• DI4. I will make a donation to streamers soon.