Uncovering the impact on environmental challenges through the predictors of m-tourism apps adoption: SEM-NCA approaches

Abstract

This study investigates the factors influencing the adoption of m-tourism apps in Bangladesh and their impact on being free from environmental challenges. Utilizing Structural Equation Modeling (SEM) and Necessity Condition Analysis (NCA), the research explores how various predictors affect the Intention to Adopt Tourism Apps (IATA) and, subsequently, the outcome of being free from environmental challenges (FEC). SEM results reveal that Perceived Usefulness (PU), Cultural Exchange of Technology (CET), Social Amusement and Entertainment (SAE), and Tourists' Lifestyles (TL) significantly enhance IATA, with IATA having a strong positive impact on FEC. Conversely, Attitudes Towards Technology (ATT) and Government Supportive Roles (GSR) show limited influence on adoption. NCA identifies CET, IATA, SAE, and TL as critical predictors with medium to large effect sizes, particularly at higher thresholds, indicating their role as major bottlenecks. Government Supportive Roles and Tourist Technology Readiness are less influential. These findings offer valuable insights for developers and policymakers to focus on enhancing the perceived value, cultural exchange, and lifestyle compatibility of m-tourism apps to improve adoption rates and effectively address environmental challenges.