Engagement as a concept is gaining attention in research and management practices. However, there are several types of engagement depending on whether the focus is on work and jobs or companies and organizations. In this paper, we demonstrate the following three points based on comparative analysis of the concepts of work engagement and employee engagement: (a) Both terms are used with the same frequency in academic journals in the field of management, but non-academic sources consistently use “employee engagement,” while medical and nursing articles use “work engagement.” (b) “Work engagement” may be used in this way because the term originated in research of burnout among nurses working in hospitals. (c) While much research does not adequately differentiate the two concepts, they should be treated as distinct concepts due to their distinct origins and the content they measure.

Much research has been conducted on the role of the middle manager; however, most of the studies in research on this topic have focused on the middle manager's managerial work. However, upon surveying 2,183 managers at Japanese firms, we found that 87% of managers were actually engaged in nonmanagerial work. Furthermore, in extreme cases where middle managers responded that there was either not enough or too much non-managerial work, team performance was poor, while in cases where middle managers did an appropriate amount of nonmanagerial work, team performance was good.

In closed innovation (CI), each firm does R&D on its own; however, on the other hand, in open innovation (OI), companies use knowledge from external sources when necessary or let other firms use their proprietary technologies. Chesbrough (2003) pointed out the effectiveness of OI. OI is accompanied by (1) outsourcing dilemma that prevents a firm from getting a competitive advantage when rival firms can also gain the knowledge from same external sources, (2) integrator's dilemma, which occur when firms sell parts to assemblers that produce and sell the same product, thus becoming formidable rivals, and (3) modularity trap, whereby when it is necessary to redesign the total product structure due to radical product innovations, the firm which selected open modular architecture and OI cannot respond because knowledge has dispersed among firms. OI is not necessarily the most efficient choice when these dilemmas or traps exist.

In extant studies regarding value stream mapping (VSM) and cross-functional integration in Western journals, there is a deviation from the essence of lean production and flow management, which aim at overall optimization by focusing on the flow of the entire value chain as well as material and information flow, and empirical studies based on the actual state of material and information flow have not been sufficiently conducted. To proceed with the overall optimization of the material and information flow in the supply and value chain—with progress in globalization and digitalization as seen in recent Japanese manufacturing companies—it is necessary to return to basics to grasp the “actual state of the flow” by focusing on the entire material and information flow and to conduct empirical studies on factors contributing to these flows.

Recently, online black markets and anonymous virtual currencies have become the subject of academic research, and we have gained a certain degree of knowledge about the dark web. However, as terms such as deep web and dark net, which have different meanings, have been used in similar contexts, discussions related to the dark web tend to be confusing. Therefore, in this paper, we discuss the differences between these concepts in an easy-to-understand manner, including their historical circumstances, and explain the technology known as onion routing used on the dark web.

Only a small portion out of –200 steel-makers in the world can produce high-grade steel such as hot-dip galvanized steel sheets and directional electrical steel sheets. For equipment-related industry such as steel industry, technical knowledge is embodied in their equipment; thus, technology transfer and catch-up are somewhat easy. However, for high-grade steel production, steel-makers in emerging countries equipped with large-scale capital investment and state-of-the-art equipment capital investment continued to struggle. The reason for this is when a new process is added to the existing process and not just the added process, the operational parameters of all processes must be coordinated. Thus, when a number of processes increases, it leads to a massive number of combinations of operational parameters to be coordinated, thus requiring time to acquire knowledge patterns.