Identifying and Controlling Key Factors in Exhibition Effect:A Hybrid Method Combining Best-Worst Method and Regression Models

Dongshi Sun¹Mingyue Xue¹Yadan Zhao^2*

• ¹Dalian Neusoft University of Information, Dalian, China
• ²Dalian University of Foreign Languages, Dalian, China

The rapid expansion of the global exhibition economy has prompted an increasing number of enterprises to leverage exhibitions for gathering industry intelligence, expanding sales channels, and establishing brand presence. Achieving effective outcomes from these exhibitions necessitates carefully coordinating and planning numerous factors influencing exhibition success. To address persistent challenges in evaluating and enhancing exhibition outcomes, this paper introduces an integrated methodological framework that aligns the specific dynamics of the exhibition industry with a closed-loop key factor control mechanism. The framework synthesizes the Best– Worst Method (BWM) with multiple regression modeling to ensure both methodological robustness and practical applicability. BWM has been demonstrated as a reliable tool for prioritizing critical factors under conditions of limited or subjective data, thereby enabling the systematic identification of determinants that most significantly influence exhibition effectiveness. Complementarily, multiple regression analysis provides a well-established means of assessing the causal relationships between independent variables and outcome variables, allowing for empirical validation of hypothesized effects.. This mechanism, comprising the stages of identification, testing, adjustment, and optimization, was empirically tested using a case study of an aquatic products exhibition. The study identified eight initial key factors, including post-exhibition services (E4), in-exhibition management (E3), exhibition sustainability (B5), and the economic conditions of the exhibition venue (B3). Subsequently, through the application of goodness-of-fit and significance testing within the multiple regression model, factors with lower correlations to exhibition effect, specifically post-exhibition services (E4), economic conditions of the exhibition venue (B3), and exhibition investment (D1), were excluded, thereby confirming the rationality of the optimized key factors. Ultimately, systematic control of these factors was implemented based on the concept of supply chain integration. The findings of this research represent an innovative contribution to system evaluation and factor management theory, specifically tailored to the exhibition industry. The model's convenience and scientific rigor provide enterprises with an effective tool for enhancing exhibition effect.