Abstract
The present research aims to identify and prioritize the factors affecting the success and failure of platform ecosystems in technology companies. The research method is applied-developmental in terms of its objective, and exploratory mixed-methods (qualitative-quantitative) in nature. The statistical population includes 15 industry and academic experts in the qualitative section and 12 experts in the quantitative section. The data collection instrument is semi-structured interviews. For data analysis in the qualitative section, thematic analysis was conducted using MAXQDA 2024 software, and in the quantitative section, the factors were prioritized using the fuzzy Analytic Hierarchy Process (AHP). Sensitivity analysis using a four-scenario approach, along with Friedman and Spearman tests, was employed to confirm the robustness of the results. The qualitative results identified 36 open codes, 12 sub-themes, and ultimately 6 success factors and 6 failure factors. The quantitative results showed that the most important success factors are, respectively, “positive network effects” (weight 0.382), “transparent and fair governance” (0.245), and “effective management of complementors” (0.163). In contrast, the most important failure factors are “multi-homing migration” (0.351), “unsustainable revenue model” (0.228), and “reverse network effects” (0.175). The inconsistency ratios of the matrices (CR = 0.05 for success and CR = 0.07 for failure) indicate satisfactory reliability of judgments. A proposed three-layer integrated framework for success (foundational prerequisites, core drivers, sustaining factors) and failure (greatest threat, structural threats, contextual threats) was presented. Platform managers should prioritize strengthening positive network effects, governance transparency, and countering multi-homing migration.
Introduction
In recent years, the emergence and expansion of digital platforms have fundamentally transformed the landscape of technology businesses. Platform ecosystems, formed through dynamic interactions among different groups of actors (developers, suppliers, customers, and complementors), have become one of the pillars of the digital economy (Alt, 2022). Despite the remarkable success of some global platforms, many platform ecosystems in technology companies face a high failure rate, and only a limited number of them are able to sustain and scale (Fan et al., 2021). This issue is of particular importance in Iran due to the rapid growth of platforms such as Snapp, Digikala, Torob, and Albatros, coinciding with challenges of survival and sustainable profitability. According to industry reports, the customer churn rate in Iranian platforms is estimated at over 40% annually on average, indicating structural problems in governance, revenue, and user interaction models. Previous research in the field of platform ecosystems has primarily focused on aspects of architecture, governance, and competitive strategy (Engert et al., 2022). Some studies have addressed the role of complementors (Blasco-Arcas et al., 2020; Deilen & Wiesche, 2021), while others have emphasized the importance of platform leadership (Leong et al., 2019). In this regard, recent studies published in reputable domestic journals have emphasized the need to localize success and failure models in digital contexts. For example, Torkanloo et al. (2025), in a study titled “Increasing Purchase Intention in Chain Retail Stores Using an Integrated Channel Strategy,” demonstrated that customer experience and perceived risk play a decisive role in the success of digital channels. Additionally, Aalami et al. (2025), in designing a customer experience model in the retail industry with an emphasis on Generation 4.0, highlighted the importance of bilateral interactions and co-creation of value as the primary drivers of success.
In this context, the main research question is formulated as follows: How can the factors affecting the success and failure of platform ecosystems in technology companies be identified and prioritized?
Theoretical Framework
Platform Ecosystem
A platform ecosystem refers to a set of independent and semi-independent actors organized around a shared technological core (the platform) who, through repeated interactions, exchanges, and mutual collaboration, create value beyond the sum of the individual components. Unlike traditional hierarchical organizations, which are based on ownership and direct control, platform ecosystems operate through indirect orchestration and network governance (Kretschmer et al., 2022).
Rong et al. (2025) examined the all-in-one platform strategy, focusing on building a multi-product ecosystem to lock in users. The results showed that features such as the integration of diverse services, inter-product synergies, and centralized data management influence platforms’ ability to create sustainable user dependence and strengthen their competitive position in multi-sided markets by increasing switching costs and enhancing a unified user experience.
Schreieck et al. (2022) investigated the governance of platform ecosystems through inward and outward governance in traditional companies, using SAP as a case study. The findings indicated that factors such as managing conflicts between internal units, designing open application programming interfaces (APIs), and creating reward mechanisms for business partners, through their influence on platform governance structure, affect the success of leading firms in building innovation ecosystems and maintaining competitiveness in digital markets.
Research Methodology
The research method, in terms of objective, is applied-developmental, and in terms of nature, it is exploratory mixed-methods (qualitative-quantitative). The statistical population in the qualitative section consisted of 15 industry and academic experts, while in the quantitative section it included 12 experts. The data collection tool was semi-structured interviews.
Research Findings
For data analysis, thematic analysis was employed in the qualitative section using MAXQDA 2024 software. In the quantitative section, factors were prioritized using the fuzzy Analytic Hierarchy Process (AHP). Four-scenario sensitivity analysis, along with Friedman and Spearman tests, were used to confirm the robustness of the results.
The qualitative analysis revealed 36 open codes, 12 sub-themes, and ultimately identified 6 success factors and 6 failure factors.
The quantitative analysis indicated that the most significant success factors, in order of importance, are: “Positive network effects” (weight 0.382), “Transparent and fair governance” (0.245), “Effective management of complementors” (0.163). Conversely, the most critical failure factors identified are: “Multi-homing migration” (0.351), “Unsustainable revenue model” (0.228), “Reverse network effects” (0.175).
The matrix inconsistency ratios (CR=0.05 for success and CR=0.07 for failure) suggest a favorable reliability of the judgments.
A proposed three-layer integrated framework for success (foundational prerequisites, core drivers, sustaining factors) and failure (greatest threat, structural threats, contextual threats) was presented. Platform managers should prioritize strengthening positive network effects, governance transparency, and countering multi-homing migration.
Conclusion
The present research was conducted with the aim of identifying and prioritizing the factors affecting the success and failure of platform ecosystems in technology companies. The findings are consistent with the results of Zeng et al. (2025), Bontems & Chaves (2025), Tiwana & Konsynski (2024), Schreieck et al. (2024), Busalim et al. (2024), Hao & Fan (2026), Rong et al. (2025), Schreieck et al. (2022), Marković et al. (2022), Gawer (2022), Mirzaee Azandariani & Arya (2022), Jalali (2021), Kretschmer et al. (2022), and Calabrese et al. (2021). Specifically, Rong et al. (2025) demonstrated that features such as the integration of diverse services, inter-product synergy, and centralized data management—by increasing switching costs and enhancing a unified user experience—influence the ability of platforms to create sustainable dependence and stabilize their competitive position in multi-sided markets.
Based on the research findings, the following recommendations are proposed:

Strengthening positive network effects alongside managing reverse effects: Utilize loyalty mechanisms, quality ranking, and supply-demand balancing to prevent value degradation caused by network congestion.
Transforming governance transparency into a competitive advantage: Publicly declare transparent commission rules, fair pricing, and dispute resolution procedures, and institutionalize adherence to these standards.