Determinants of Donation Intention in Live Streaming–Model Inventory

Abstract

The Determinants of Donation Intention in Live Streaming–Model (Chou & Nguyen, 2023) was created for a study that examines how the development of live streaming relationships, supported by live streaming interactions, influences post-adoption behaviors, building upon the dedication-constraint framework. The proposed items were adapted from previous research. The original scales were translated into Chinese by a Taiwanese bilingual expert, followed by a back-translation technique (Schaffer & Riordan, 2003) to ensure consistency between the Chinese and English versions. Minor modifications were made to the wording to align with the live streaming context and ensure clear understanding for respondents. A pilot test was conducted with individuals experienced in live streaming before finalizing the questionnaire. The resulting measure was administered to a sample of experienced live streaming users in Taiwan and evaluated using structural equation modeling. The study reported findings regarding reliability and validity.

Keywords

Behavioral Compulsion; Cognitive Preoccupation; Dedication-Constraint Framework; Donation Intention; Entertainment; Live Streaming Interactions; Personalization; Responsiveness; Social Feedback

Authors

Chou, Shih-Wei; Nguyen, Trieu

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Purpose

The purpose of this measurement model is to assess the determinants of donation intention in live streaming.

Validity

Convergent and Discriminant validity: The results demonstrated favorable convergent validity, with all factor loadings exceeding 0.7 and being significant. Composite Reliability (CR) values ranged from 0.877 to 0.929. The square roots of the Average Variance Extracted (AVE) for each construct, ranging from 0.829 to 0.892, were greater than all correlations among the constructs (Fornell & Larcker, 1981). Additionally, all Heterotrait-Monotrait Ratio of Correlations (HTMT) values were below the cut-off value of 0.85 (Henseler et al., 2015). The study also found that the loading of each measurement item on its assigned construct was greater than its loading on other constructs, which validated strong discriminant validity (Hair Jr et al., 2021).

Reliability

Internal consistency: Cronbach’s alpha values were greater than 0.70 (Hair Jr et al., 2021), and CR values ranged from 0.877 to 0.929, indicating good internal consistency.

Factor Analysis

Common method bias: Harman’s single-factor test was used to detect the common method bias (CMB) issue. The findings revealed that the variance of the first factor constituted only 41.15% of the total variance, which is below the benchmark of 50% (Podsakoff et al., 2012).

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

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Authors

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

Affiliation:
Chou, Shih-Wei: National Kaohsiung University of Science and Technology Department of Information Management
Nguyen, Trieu: National Kaohsiung University of Science and Technology College of Management, IMBA Program

Email addresses:
Chou, Shih-Wei: [email protected]
Nguyen, Trieu: [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

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

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.