Purpose: Current knowledge management (KM) systems cannot be used effectively for decision-making because of the lack of real-time data. This study aims to discuss how KM can benefit by embedding Internet of Things (IoT). Design/methodology/approach: The paper discusses how IoT can help KM to capture data and convert data into knowledge to improve the parking service in transportation using a case study. Findings: This case study related to intelligent parking service supported by IoT devices of vehicles shows that KM can play a role in turning the incoming big data collected from IoT devices into useful knowledge more quickly and effectively. Originality/value: The literature review shows that there are few papers discussing how KM can benefit by embedding IoT and processing incoming big data collected from IoT devices. The case study developed in this study provides evidence to explain how IoT can help KM to capture big data and convert big data into knowledge to improve the parking service in transportation.

E-learning is learning through the use of technologies. It is growing at a rapid pace. Today more organizations are taking up e-learning. While e-learning technology has matured considerably since its inception, there are still many problems that practitioners And when come to implementing e-learning. One of the main problems is the complexity of integrating these systems with content and with other business systems. If e-learning is to be successful, it is important that we must provide greater access to education and support educational programs that reflect broader strategic business goals. It is our belief that the e-learning industry should learn key lessons from e-business. This paper discusses the limitations of current e-learning systems and how an e-learning ecosystem can help organizations to achieve the benefits of an integrated approach to develop e-learning systems.

The Internet of Vehicles (IoV) has generated great interest among researchers from different disciplines as it is multidisciplinary research. Sustainability for the IoV requires solutions from different perspectives, particularly in the context of environmental, social, and corporate governance. This review paper examines each of the mentioned perspectives of IoV research which were conducted among at least one of these three perspectives. On the one hand, this allows determining how widely research on the IoV system has been conducted. Moreover, it shows the directions of research on the IoV. On the other hand, it determines whether and how the IoV research is linked to each of the perspectives separately and analyses this link from a global perspective as well; i.e., it analyses the survey data in terms of the data’s relationship to all the perspectives as a group. As one of the research results, a conceptual model of IoV systems allocating the ESG perspectives was developed. The current research has shown that consideration of IoV systems in the context of these three perspectives (treated both individually and collectively) is still limited. A balanced approach towards these IoV systems is still required. Therefore, the paper consists of a survey of the current research related to the sustainability of the IoV from the three mentioned perspectives, aiming to give a balanced view of the importance of the three perspectives for IoV systems.

Purpose: This paper aims to define a conceptual framework for transforming Big Data into organizational value by focussing on the perspectives of service science and activity theory. In coherence with the agenda on evolutionary research on intellectual capital (IC), the study also provides momentum for researchers and scholars to explore emerging trends and implications of Big Data for IC management.Design/methodology/approach: The paper adopts a qualitative and integrated research method based on a constructive review of existing literature related to IC management, Big Data, service science and activity theory to identify features and processes of a conceptual framework emerging at the intersection of previously identified research topics.Findings: The proposed framework harnesses the power of Big Data, collectively created by the engagement of multiple stakeholders based on the concepts of service ecosystems, by using activity theory. The transformation of Big Data for IC management addresses the process of value creation based on a set of critical dimensions useful to identify goals, main actors and stakeholders, processes and motivations.Research limitations/implications: The paper indicates how organizational values can be created from Big Data through the co-creation of value in service ecosystems. Activity theory is used as theoretical lens to support IC ecosystem development. This research is exploratory; the framework offers opportunities for refinement and can be used to spearhead directions for future research.Practical implications: The paper proposes a framework for transforming Big Data into organizational values for IC management in the context of entrepreneurial universities as pivotal contexts of observation that can be replicated in different fields. The framework provides guidelines that can be used to help organizations intending to embark on the emerging paradigm of Big Data for IC management for their competitive advantages.Originality/value: The paper’s originality is in bringing together research from Big Data, value co-creation from service ecosystems and activity theory to address the complex issues involved in IC management. A further element of originality offered involves integrating such multidisciplinary perspectives as a lens for shaping the complex process of value creation from Big Data in relationship to IC management. The concept of how IC ecosystems can be designed is also introduced.

Many factors can influence students’ attitudes and their readiness to learn, especially with respect to learning physics online. Traditional online learning, where the teacher is the sole speaker, is inappropriate for learning physics because there must be live demonstrations and activities connecting theories with real world experiences. Online learning for physics must be active and engaging. Students would find the traditional form of online instruction difficult, because there is no physical social interaction between teacher and students. In our teaching work, we have found that factors such as computer skills/ICT skills, learning preferences, prior knowledge and motivation are important for students’ learning. What are the perceptions and attitudes of learners regarding these factors? The aim of this paper is to investigate the attitudes of students’ responses to computer/ICT skills, learning preferences, prior knowledge, and motivation pre-online learning and post-online learning in a case study. The research used a hierarchical regression for data analysis across a sample of young respondents who studied physics at Labuan Matriculation College, i.e., pre-university, in year one of their enrolment. The study involved two phases. A survey was conducted to assess the attitudes of the students prior to the implementation of active online learning. The pre-survey results showed that students considered learning preference and motivation to be important factors that would influence their active online learning. Post-survey responses and views communicated after completion of the learning revealed that all four factors have positive influence in their learning. Principles from neuroscience were used to explain why these four factors were important. The paper also provides guidelines on how teachers can use principles from neuroscience to help students to improve active online learning based on these four factors.