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Selecting Local Partners for Chinese Technology Firms Entering Saudi Arabia and the United Arab Emirates: An Explainable, Uncertainty-Aware Framework Using Public Data

Milad Mohammadi1, Wensong Zhang1
1School of Economics and Management, Beijing Jiaotong University, Beijing, China
DOI: https://doi.org/10.70517/revcc2624164
Published: 15/10/2026
: Milad Mohammadi, Wensong Zhang. Selecting Local Partners for Chinese Technology Firms Entering Saudi Arabia and the United Arab Emirates: An Explainable, Uncertainty-Aware Framework Using Public Data. Revista Cultura Científica, 2026 Issue 24. pg. 1958-1974.

Abstract

Selecting an appropriate local partner is a critical yet underexplored stage of international market entry for technology firms, particularly when partner suitability depends on financial strength, technological complementarity, institutional access, governance quality, and reciprocal strategic value. This study develops an explainable and uncertainty-aware Value–Access–Integrity–Reciprocity (VAIR) framework for Chinese technology firms seeking local partners in Saudi Arabia and the United Arab Emirates. Eleven candidate firms were evaluated using publicly verifiable corporate data, with the evidence harmonized through a two-market comparability protocol and the candidate set screened using explicit non-compensatory audit and going-concern veto rules. Partner suitability was assessed under three Chinese entrant archetypes—AI/data infrastructure, cloud/cybersecurity, and state-linked digital infrastructure—using reciprocal-fit modelling and Stochastic Multicriteria Acceptability Analysis (SMAA) with 100,000 Monte Carlo iterations per primary scenario. In Saudi Arabia, ELM ranked first in all three base scenarios, with rank-one acceptability probabilities of 63.86%–72.75%, although 2P became the leading CN-AI candidate under acquisition-adjusted revenue-growth sensitivity. In the UAE, du led all hierarchical VAIR scenarios with rank-one probabilities of 51.46%–56.90%, while e& became the preferred candidate when the criterion-level weights were sampled without the hierarchical VAIR constraint. A retrospective consistency check against five publicly documented Chinese–local technology relationships, including announcements and memoranda of understanding, showed that four corresponding local firms fell within the top three model positions. This offers only limited external plausibility, rather than predictive validation or evidence of completed alliance performance. The findings show that local partner selection is a conditional matching decision shaped by entrant type, evidence uncertainty, and preference architecture. The framework provides a transparent and reproducible approach to screening prospective partners in institutionally complex technology markets.

Keywords: local partner selection; Chinese technology firms; Saudi Arabia; United Arab Emirates; stochastic multicriteria acceptability analysis; strategic alliances

Abstract

Selecting an appropriate local partner is a critical yet underexplored stage of international market entry for technology firms, particularly when partner suitability depends on financial strength, technological complementarity, institutional access, governance quality, and reciprocal strategic value. This study develops an explainable and uncertainty-aware Value–Access–Integrity–Reciprocity (VAIR) framework for Chinese technology firms seeking local partners in Saudi Arabia and the United Arab Emirates. Eleven candidate firms were evaluated using publicly verifiable corporate data, with the evidence harmonized through a two-market comparability protocol and the candidate set screened using explicit non-compensatory audit and going-concern veto rules. Partner suitability was assessed under three Chinese entrant archetypes—AI/data infrastructure, cloud/cybersecurity, and state-linked digital infrastructure—using reciprocal-fit modelling and Stochastic Multicriteria Acceptability Analysis (SMAA) with 100,000 Monte Carlo iterations per primary scenario. In Saudi Arabia, ELM ranked first in all three base scenarios, with rank-one acceptability probabilities of 63.86%–72.75%, although 2P became the leading CN-AI candidate under acquisition-adjusted revenue-growth sensitivity. In the UAE, du led all hierarchical VAIR scenarios with rank-one probabilities of 51.46%–56.90%, while e& became the preferred candidate when the criterion-level weights were sampled without the hierarchical VAIR constraint. A retrospective consistency check against five publicly documented Chinese–local technology relationships, including announcements and memoranda of understanding, showed that four corresponding local firms fell within the top three model positions. This offers only limited external plausibility, rather than predictive validation or evidence of completed alliance performance. The findings show that local partner selection is a conditional matching decision shaped by entrant type, evidence uncertainty, and preference architecture. The framework provides a transparent and reproducible approach to screening prospective partners in institutionally complex technology markets.

Keywords: local partner selection; Chinese technology firms; Saudi Arabia; United Arab Emirates; stochastic multicriteria acceptability analysis; strategic alliances

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Milad Mohammadi
School of Economics and Management, Beijing Jiaotong University, Beijing, China
miladmoh@qq.com
Wensong Zhang
School of Economics and Management, Beijing Jiaotong University, Beijing, China

How to cite:

Milad Mohammadi, Wensong Zhang. Selecting Local Partners for Chinese Technology Firms Entering Saudi Arabia and the United Arab Emirates: An Explainable, Uncertainty-Aware Framework Using Public Data. Revista Cultura Científica, 2026 Issue 24. pg. 1958-1974.

Publication History

  • Received: 09/06/2026
  • Accepted: 17/09/2026
  • Published: 15/10/2026
Copyright © 2026, Milad Mohammadi, Wensong Zhang. Published by Revista Cultura Científica. This article is published as open access under the Creative Commons Attribution 4.0 International (CC BY 4.0) license (http://creativecommons.org/licenses/by/4.0/).

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