Clustering Iranian Railway Transportation Companies Based on Ambidextrous Marketing Dimensions: A Multigroup Structural Equation Analysis

The rapid environmental and competitive transformations in Iran’s railway transportation industry have intensified the necessity of adopting innovative marketing approaches. One such approach is ambidextrous marketing, which enables organizations to exploit existing capabilities while simultaneously exploring new opportunities. The aim of this study is to cluster national railway companies based on ambidextrous marketing dimensions and to compare the structural relationships among the identified groups. Data were collected from ۱۶۹ valid questionnaires and analyzed using a combination of clustering methods (Ward and K-means) and Partial Least Squares Structural Equation Modeling (PLS-SEM). The analysis identified three distinct clusters: technology leaders, characterized by a focus on innovation and service speed; structured customer-oriented firms, emphasizing organizational culture and human relationship management; and resource-constrained firms, facing challenges related to limited human capital and technological capabilities. The multigroup analysis revealed that the intensity and pattern of variable effects differ across clusters: technology and service speed are key drivers in the technology leaders cluster, organizational culture plays a central role among structured customer-oriented firms, and formal structure along with environmental pressures hinder value creation in resource-constrained firms. Despite these differences, ambidextrous marketing in all clusters led to financial and social outcomes through the pathways of value creation and knowledge management. The findings of this research, while confirming ambidexterity theory, highlight the need for developing strategies tailored to the specific characteristics of each cluster. The study provides practical insights for policymakers and railway managers in designing marketing programs, advancing digitalization, and promoting sustainable industry development. The rapid environmental and competitive transformations in Iran’s railway transportation industry have intensified the necessity of adopting innovative marketing approaches. One such approach is ambidextrous marketing, which enables organizations to exploit existing capabilities while simultaneously exploring new opportunities. The aim of this study is to cluster national railway companies based on ambidextrous marketing dimensions and to compare the structural relationships among the identified groups. Data were collected from ۱۶۹ valid questionnaires and analyzed using a combination of clustering methods (Ward and K-means) and Partial Least Squares Structural Equation Modeling (PLS-SEM). The analysis identified three distinct clusters: technology leaders, characterized by a focus on innovation and service speed; structured customer-oriented firms, emphasizing organizational culture and human relationship management; and resource-constrained firms, facing challenges related to limited human capital and technological capabilities. The multigroup analysis revealed that the intensity and pattern of variable effects differ across clusters: technology and service speed are key drivers in the technology leaders cluster, organizational culture plays a central role among structured customer-oriented firms, and formal structure along with environmental pressures hinder value creation in resource-constrained firms. Despite these differences, ambidextrous marketing in all clusters led to financial and social outcomes through the pathways of value creation and knowledge management. The findings of this research, while confirming ambidexterity theory, highlight the need for developing strategies tailored to the specific characteristics of each cluster. The study provides practical insights for policymakers and railway managers in designing marketing programs, advancing digitalization, and promoting sustainable industry development.

کلیدواژه ها:

Railway transportation ، ambidextrous marketing ، multigroup analysis ، clustering