Identifying and explaining the antecedents of customer experience management in the maritime transportation industry: A mixed-methods approach
نویسندگان
1 Department of Business Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
2 Department of Business Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
3 Department of Business Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran
doi
10.22075/ijnaa.2024.33957.5066چکیده
This research aims to identify and explain the components and antecedents of customer experience management in the maritime transport industry based on the customer journey approach. To this end, considering the theoretical gap in the research topic, a qualitative approach of grounded theory was employed. Sampling was conducted purposefully and theoretically. Data was collected through in-depth semi-structured interviews with executives of manufacturing, trading, and forwarding companies that use the container line of the Islamic Republic of Iran Shipping Lines for export and import shipments. After conducting 22 interviews, theoretical adequacy was achieved. To assess the research's trustworthiness, the criteria of credibility, dependability, confirmability, and transferability were used. Based on the results of data coding and analysis, the antecedents of customer experience management in maritime transport were identified and analyzed in the form of causal conditions (brand position, maritime transport quality, and container terminal performance quality), intervening conditions (organizational factors, agency performance, information technology characteristics, and competitiveness), and contextual conditions (political and economic environment). In the quantitative phase, data were collected through a questionnaire and analyzed using confirmatory factor analysis and structural equation modelling with SmartPLS3 software. The obtained results confirmed the concepts and categories identified in the proposed research model. Furthermore, the goodness-of-fit indices indicated that the data and the structure of the research model had a good fit and were supported.