The advent of digitalisation has transformed economies into more integrated, but increasingly complex systems. This new trend has brought dynamic changes in the manufacturing sector through advanced ICT infrastructure, smart factories, digitally-controlled logistics, and skilled ICT-labour. The impacts of the digital economy on manufacturing could be best illustrated through “Industry 4.0.” With this wave of technological advancement, countries aim to establish an industrial ecosystem where every manufacturing process and function is connected and interacts through digital networks. Industry 4.0 presents opportunities for Emerging Asia, as the region has emerged as a fast-growing manufacturing hub and particularly a production base for ICT goods. However, growing production capacity, increased exports, and increases in FDI in the field of ICT goods manufacturing have so far contributed little to the development and diffusion of ICT. A huge gap exists in the ICT uptake amongst countries and between small and large firms. This paper highlights the level of Industry 4.0 readiness of Emerging Asia and key factors that determine its enhancement.
Myanmar’s current power situation remains severely constrained despite being richly endowed in primary energy sources. With low levels of electrification, the demand for power is not adequately met. Cooperation in energy has been a major focus of future initiative for all developed and developing nations. If we want to solve climate change, and change our energy infrastructure, we need to be innovative and entre-preneurial in energy generation. This paper will help us in examining Bayesian MCMC Analysis for the parameters estimation among the arrival rates of disaster occurrences, firm’s expected income-based electricity tariffs, and estimated R&D investment expenses in new energy industry. Focusing on Japan’s electric power business, we would like to search the potential for innovative initiatives in new technological energy industry for the regional development and ecological sustainability in Myanmar.
The wave of the 4th Industrial Revolution was announced by Schwab Klaus at the 2016 World Economic Forum in Davos, and prospects and measures with the future society in mind have been put in place. With the launch of the Moon Jae-in administration in May 2017, Korea has shifted all of its interest to Big Data, which is one of the most important features of the 4th Industrial Revolution. In this regard, this study focuses on the role of the public sector, explores related issues, and identifies an agenda for determining the demand for ways to foster Big Data ecosystem, from an objective perspective. Furthermore, this study seeks to establish priorities for key Big Data issues from various areas based on importance and urgency using a Delphi analysis. It also specifies the agenda by which Korea should exert national and social efforts based on these priorities in order to demonstrate the role of the public sector in reinforcing the Big Data ecosystem.
This study suggests a new approach for identifying core robot tech-nologies based on technological cross-impact. Specifically, the approach applies data mining techniques and multi-criteria decision-making methods to the co-classification information of registered patents on the robots. First, a cross-impact matrix is constructed with the confidence values by applying association rule mining (ARM) to the co-classification information of patents. Analytic network process (ANP) is applied to the co-classification frequency matrix for deriving weights of each robot technology. Then, a technique for order performance by similarity to ideal solution (TOPSIS) is employed to the derived cross-impact matrix and weights for identifying core robot technologies from the overall cross-impact perspective. It is expected that the proposed approach could help robot technology managers to formulate strategy and policy for technology planning of robot area.
The Korean government has focused on universities or colleges as the main targets of its startup policy since the 2010s. However, the performance is not so good, with a low survival rate. The purpose of this study is to examine the factors that underpin the success of student startups. First, through a review of the literature, this study compared the success factors of student startups with those of venture startups, which means the general startup sector, as well as youth startups, also a focus of startup policy targeting youngsters outside universities or colleges. Second, we analyzed case studies of startup companies connected University H. The literature review showed that the main target of student startups is the employment of university students. There is a lack of studies on success factors; existing studies only emphasize the entrepreneurship of students. The results of case studies showed several factors of success similar to those of general venture startups: founders, business model and resources including team, and mentoring.
Providing free primary care to everyone is an important goal pursued by many countries under universal health care programs. Countries like India need to efficiently utilize their limited capacities towards this purpose. Unfortunately, due to a variety of reasons, patients incur substantial travel and out-of-pocket expenses for getting primary care from publicly-funded facilities. We propose a set-covering optimization model to assist health policy-makers in managing existing capacity in a better way. Decision-making should consider upgrading centers with better potential to reduce patient expenses and reallocating capacities from less preferred facilities. A multinomial logit choice model is used to predict the preferences. In this article, a brief background and literature survey along with the mixed integer linear programming (MILP) optimization model are presented. The working of the model is illustrated with the help of numerical experiments.
A considerable amount of research has been directed at subsistence markets in the recent past with the belief that these markets can be tapped profitably by marketers. Consequently, such markets have seen the launch of a number of innovative products. However, marketers of such forecasts need timely and accurate forecasts regarding the diffusion of their products. The Bass model has been widely used in marketing management to forecast diffusion of innovative products. Given the idiosyncrasies of subsistence markets, such forecasting requires an understanding of effective estimation techniques of the Bass model and their use in subsistence markets. This article reviews the literature to achieve this objective and find out gaps in research. A finding is that there is a lack of timely estimates of Bass model parameters for marketers to act on. Consequently, this article sets a research agenda that calls for timely forecasts at the takeoff stage using appropriate estimation techniques for the Bass model in the context of subsistence markets.
Purpose: Companies in haste for higher consumers’ preference tend to appear as ‘green’ and mislead about environmental concerns, which are termed as “Greenwashing.” The purpose of the study is to investigate the consumer perception on greenwashing activities and analyze its impact on green brand image, green brand loyalty and green brand trust among Indian consumers. Design/methodology: The study makes use of a written questionnaire method to collect survey data from approximately 500 consumers all over India. The study uses Structural Equation Modeling (SEM) to study the hypothesized relationship between constructs affected by greenwashing based on consumer perspective in the Indian context. Findings: The study shows that Indian consumers are becoming aware of greenwashing activities, which have a negative impact on green brand trust and undermines green brand image and green brand loyalty. Implications: The study results are beneficial to policy-makers, researchers, practitioners, and managers to create awareness among Indian consumers on greenwashing activities.