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This paper considers the usability of mobile applications operating within a new logistics domain referred to as logistics in life (LIL). The LIL sector has primarily been capitalized on by logistics startups which develop mobile applications or “apps” to provide customized services that penetrate niche spaces outside the reach of traditional logistics firms. The objective of this study is to evaluate whether LIL apps meet usability standards that satisfy users’ experiences. As a way to improve usability, problems should be identified through proper measurement and evaluation methods. To derive usability scores, usability testing targeting representative apps from Korea and foreign countries was conducted. The relationship between usability and user interest for each app was determined through big data analytics followed by recommended improvement strategies.

This paper provides human resource managers with important guidelines when applying expert systems to human resource domains, by reviewing system characteristics, potential benefits and limitations, and appropriate domains to be selected, based on the literature. Structural and procedural aspects of expert systems development in human resource management (represented by a wheel model) and problem descriptions of each expert system respectively are important research results in this paper. We also show semantic networks or semantic nets used for a knowledge representation methodology on such major human resource management activities as human resource planning, recruiting, compensation and labor-management relations. Knowledge representation is a key component in the development stages of expert systems.

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Effective job placement as a sphere of employment management in an organization is generally regarded as critical for positive maintenance of the organization and for a resulting increase in company-wide productivity. Managers of a personnel department in an organization have commonly tried various approaches to achieve philosophy effective job placement as a crucial part of employment management based on the principle of right-person-in-right-place. However, these approaches are usually dependent on rule of thumb without consideration of the methodologies for identifying aptitudes and personalities of employees. Therefore, more appropriate methodologies and subsequent computer-aided systems are required to evaluate current employees' potential and appropriate placement. This study, as a phase of developing the entire employment management systems in this organization (POSCO), suggests an expert system leading to a running system as a means for effective job placement. We also discuss methodologies to be applied for each lifecycle in constructing the expert system, especially focusing on the main components of the expert system such as structure of rule base, inference engine, and user interface.

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