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The socio-technical engine driving hotel live streaming engagement: insights from PLS-SEM, NCA, and cIPMA
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  • Published: 07 March 2026

The socio-technical engine driving hotel live streaming engagement: insights from PLS-SEM, NCA, and cIPMA

  • Yi-Man Teng1,
  • Kun-Shan Wu2 &
  • Zhezhou Li1 

Humanities and Social Sciences Communications , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Business and management
  • Economics
  • Finance
  • Information systems and information technology

Abstract

Grounded in socio-technical systems theory and media absorption perspectives, this study investigates how social and technical affordances facilitate absorption experience (AE) as a psychologically immersive state of focused attention during hotel live streaming e-commerce (LSE), and how AE subsequently shapes consumer engagement behaviors, including both monetary (e.g., purchase intention) and non-monetary (e.g., liking, commenting, sharing). Drawing on survey data from 334 live streaming viewers, this study utilizes a multi-method analytical framework comprising partial least squares structural equation modeling, necessary condition analysis, and combined importance–performance map analysis. The outcomes verify that social presence, real-time interactivity, metavoicing, and guidance shopping significantly enhance AE, which in turn drives consumer engagement behaviors. The results indicate AE functions as a pivotal mediating mechanism between socio-technical characteristics and consumer engagement outcomes, with social presence emerging as a critical necessary condition for both monetary and non-monetary engagement behaviors. The integrated methodology separates ‘should have’ from ‘must-have’ components, offering a strong foundation for examining asymmetric causal linkages. The findings contribute to hospitality and live streaming e-commerce literature by theorizing AE as a context-appropriate immersive state in hotel live streaming and elucidating how socio-technical systems shape both monetary and non-monetary engagement behaviors, an area that remains insufficiently explored.

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Data availability

All data generated or analyzed during this study are included in this published article and its supplementary information file.

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Acknowledgements

We would like to acknowledge the financial support provided by the Project of Fujian Social Science Foundation in 2025, “Research on the Mechanism and Path of Generative Artificial Intelligence Empowering the Development of New Quality Productivity in Fujian Province” (No. FJ2025C036).

Author information

Authors and Affiliations

  1. International Business School, Fuzhou University of International Studies and Trade, Fuzhou city, China

    Yi-Man Teng & Zhezhou Li

  2. Department of Business Administration, Tamkang University, New Taipei City, Taiwan

    Kun-Shan Wu

Authors
  1. Yi-Man Teng
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  2. Kun-Shan Wu
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Contributions

Teng, YM: conceptualization, writing—original draft, interpretation of the results, validation, and supervision. Wu, KS: conceptualization, data curation, methodology, formal analysis, writing—original draft, and writing—review and editing. All authors have read and agreed to the published version of the manuscript. Zhezhou Li: visualization, review, and editing.

Corresponding author

Correspondence to Kun-Shan Wu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethical approval

Ethical approval for this study was obtained from the Ethics Center of the International Business School at Fuzhou University of International Studies and Trade. On January 7, 2025, prior to the commencement of data collection, the Center confirmed that the study was exempt from IRB review (Regulation ID: FUIST-IBS-240315), as it involved anonymous survey data with minimal risk and no identifiable information, clinical interventions, or biological samples. As the exemption was confirmed prior to the commencement of data collection, it does not represent retrospective ethical approval. The study complied with relevant institutional and national guidelines for non-interventional human subjects research, including the Declaration of Helsinki (1964 and amendments). The exemption applied to the study design, data collection, and management protocols.

Informed consent

Informed consent was obtained from all participants prior to participation during the data collection period from 1 March to 15 April 2025. Before accessing the questionnaire, respondents were required to indicate their voluntary agreement after being fully informed in writing about the purpose of the study, research procedures, the voluntary nature of participation, their right to withdraw at any time without penalty, and the measures implemented to ensure anonymity and data confidentiality.

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Appendix 1

Appendix 1

Table 7

Table 7 Study variables and items with means, standard deviations, skew, and kurtosis.
Full size table

Appendix 2. Reliability and validity test results

Tables 8, 9

Table 8 Results of reliability and convergent validity analysis.
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Table 9 Results of discriminant validity analysis.
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Appendix 3. NCA scatter plots with CE-FDH ceiling\

Figure 5

Fig. 5
Fig. 5
Full size image

A NCA scatter plots with CE-FDH ceiling (social presence vs purchase intention). B NCA scatter plots with CE-FDH ceiling (real-time interactivity vs purchase intention). C NCA scatter plots with CE-FDH ceiling (visibility vs purchase intention). D NCA scatter plots with CE-FDH ceiling (metavoicing vs. purchase intention). E NCA scatter plots with CE-FDH ceiling (guidance shopping vs purchase intention). F NCA scatter plots with CE-FDH ceiling (absorption experience vs purchase intention). Source: Authors’ own calculations using SmartPLS 4 software.

Appendix 4. Necessity Effect Sizes

Table 10

Table 10 CE-FDH-Derived necessity effect sizes.
Full size table

Appendix 5. Bottleneck tables

Tables 11, 12

Table 11 Bottleneck tables for PI and NME in actual values (recalibrated PLS-SEM latent construct values onto a 0–100 metric).
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Table 12 Bottleneck tables for PI and NME in actual values (based on the rescaled PLS-SEM latent variable scores from 0 to 100) and the percentiles of antecedent constructs.
Full size table

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Teng, YM., Wu, KS. & Li, Z. The socio-technical engine driving hotel live streaming engagement: insights from PLS-SEM, NCA, and cIPMA. Humanit Soc Sci Commun (2026). https://doi.org/10.1057/s41599-026-06914-9

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  • Received: 21 July 2025

  • Accepted: 27 February 2026

  • Published: 07 March 2026

  • DOI: https://doi.org/10.1057/s41599-026-06914-9

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