Purpose – The purpose of this paper is to investigate the factors of m-learning approaches which can be used for enterprise resource planning (ERP) system training and to propose a theoretical framework for m-learning of ERP systems. Design/methodology/approach – A literature review of several theories relating to success factors for mobile learning (m-learning) and electronic learning (e-learning) are analysed and a theoretical framework of success factors for m-learning of ERP systems is proposed. Two field studies are undertaken to identify the features of e-learning and m-learning systems which users enjoyed and which related to the factors identified in the theoretical framework. The technology acceptance model (TAM) was used to evaluate the acceptance, usefulness and perceived ease of use (PEOU) of the two systems evaluated in the field study, the openSAP e-learning application and the SAP Learn Now m-learning application. Findings – The results confirmed several of the theoretical elements identified in the framework and the m-learning system was rated positively for PEOU and perceived usefulness (PU). The findings confirmed other studies showing the importance of the quality of course content in e-learning and m-learning projects. Research limitations/implications – The empirical study was limited to a small number of participants in higher education. However, a deeper understanding of the factors influencing m-learning for ERP systems was obtained. Practical implications – The study provides a valuable practical contribution because the framework can be used in the improved design of an ERP m-learning approach, which in turn can lead to an improvement in ERP training and education programmes and ultimately ERP project success. Originality/value – Several studies propose the use of m-learning systems. However, research related to the factors impacting on m-learning projects for ERP system training is limited. The paper presents original work and the results provide a valuable contribution to several theories of m-learning.
In today's global, competitive economy, downtime has been identified as a key performance indicator for field service organisations. The emergence of an Internet of Things (IoT) has brought new enhancement possibilities to various industries such as the manufacturing and field service industry. This paper provides a vision and motivation for using IoT in Field Service Management (FSM) in order to address data quality and service delivery issues. The theory of information quality was used to undergird the research and a model for the optimisation of downtime management in the field service industry using the IoT is proposed. The model was used to drive the design of a "proof of concept" prototype, the KapCha prototype. The paper also includes a report on an empirical study of the application of the proposed IoT model in FSM. The experiment findings showed that the prototype reduced the round trip delay time for sending and receiving data and was scalable. As a result, access to quality information supporting advanced data analytics and artificial intelligence was provided. Therefore, service technicians can be alerted more quickly as soon as any potential technical problems occur. In turn improved diagnostics and more efficient decision making can be achieved. The model and the lessons learned provide valuable guidance to other researchers and fill the gap in research of empirical studies conducted on IoT implementations.
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