Unified implementation framework for big data analytics and internet of things-oriented transportation system: a case study of transportation system in Malaysia

Internet of Things (IoT) is the new dawn technology that describes how data, people, and interconnected physical objects act based on communicated information, and big data analytics have been adopted by diverse domains for varying purposes. The transportation industry is also an early adopter, with significant attendant effects on its processes of tracking shipment, freight monitoring, and transparent warehousing. Previous studies have only investigated the business, infrastructure/technology, managerial, technical expertise, and government policy as factors or/and models separately. This study aims to identify the implementation success factors of Big Data analytics and IoT oriented transportation system, guided by the comprehensive models of IoT success factors, MOP (Multiple-Open-Platform), and the MOA (Motivation-Opportunity-Ability) as a research theoretical framework. This qualitative research employed a systematic literature review (SLR) and in-depth interviews in a case study involving five experts within the field of Big Data Analytics and IoT based transportation system. SLR was used to seek, identify and collate related past studies on (a) business, (b) infrastructure/technology, (c) technical expertise, (d) administrative/managerial, and (e) government policy and regulation, as components of the implementation framework for Big Data Analytics and IoT-oriented transportation system. The search began in the IEEE Xplore, Emerald, Springer, Science Direct, and WoS databases through their search boxes. In this search, a mix of keywords has been used, which contained “business strategy/need for Big data-based (and IoT) transportation systems” among others in different variations, combined by the “OR” operator. The in-depth interview used to identify the core elements of the implementation factors for Big Data Analytics and IoT-oriented transportation system. The iDeria system (Vehicle/fleet monitoring and tracking system) has been selected as the case study due to its successful implementation of Big Data Analytics and IoT-oriented transportation system. iDeria was selected based on its ability to foster data-driven culture when compared with other fleet management systems. This study used a purposive sampling technique to select the informants who can provide detailed information about the elements of Big Data analytics and IoT-oriented transportation system. The result showed that the SLR findings revealed five (5) factors for the unified implementation success framework for Big Data analytics and IoT-oriented transportation system, while the qualitative data analysis showed four (4) factors. It is, however, noteworthy that, the four factors found from the qualitative data analysis cover the 5 found in the SLR. For clarity, the technical expertise which is not among the factors listed in the qualitative data analysis findings is captured under the technology factor as its sub-theme. The outcomes of this research have been presented as a unified implementation framework for Big Data analytics and IoT based transportation system which consist of four implementation models namely business, technology, management, and government policy. Overall this study indicates that customer satisfaction can better be achieved and improved when all these factors and their elements are identified significantly.