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Purpose This paper aims to examine the impact of knowledge management capability (KMC) on supply chain management practices (SCMPs), organizational learning (OL) and organizational performance (OP) in the Malaysian logistics industry. Design/methodology/approach The data were gathered using a self-administered questionnaire from the management team in the logistics companies. A total of 412 questionnaires were collected out of which 183 responses were included in the data analysis. This represents a response rate of 44.4%. The respondents were those with managerial and/or supervisory experience where their job title or functions included Managers, Head of the Department, Owners, Chief Executive Officer, Senior Executive Officer and at the very least, Assistant Manager or Supervisors. To investigate the correlations between all the elements (e.g. KMC, OL, SCMPs and OP), this study used different analysis techniques including correlation analysis, reliability and validity test, as well as a structural model. Findings The results indicated that KMC is strongly correlated and has a positive impact on SCMPs in addition to being positively correlated to OL and OP. Also, OL is positively related to OP and SCMPs. Research limitations/implications The findings of this research contribute to the growing body of literature linking KMC with SCMPs, OL and OP. Practical implications The findings provide insight on the importance of knowledge management and OL toward improving SCMPs within organizations. Therefore, the findings are useful for shedding light upon formulating strategies for SCMPs among the decision-makers that will ultimately enhance the overall OP. Originality/value This study meaningfully contributes to enhancing the understanding of the state of affairs of the impact of management capability on SCMPs, OL and OP in the logistics industry. Practitioners may formulate strategies to further improve the study presented here for a better implementation of knowledge management and SCMPs within their organizations.

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The advancement of mobile technologies has motivated countries around the world to aim for smarter health management to support senior citizens. However, the use of mobile health applications (mHealth apps) among senior citizens appears to be low. Thus, drawing upon user expectations, the present study examined user requirements for a senior-friendly mHealth application. A total of 74 senior citizens were interviewed to explore the difficulties they encounter when using existing mobile apps. This study followed Nielsen’s usability model to identify user requirements from five aspects, namely learnability, efficiency, memorability, error, and satisfaction. Based on the results, a guideline was proposed pertaining to usability and health management features. This guideline offers suggestions for mHealth app issues related to phrasing, menus, simplicity, error messages, icons and buttons, navigation, and layout, among others. The study also found that speech recognition technology can help seniors access information quickly. The proposed guideline and findings offer valuable input for software and app developers in building more engaging and senior-friendly mHealth apps.

This paper presents a prototype that can convert sign language into text. A Leap Motion controller was utilised as an interface for hand motion tracking without the need of wearing any external instruments. Three recognition techniques were employed to measure the performance of the prototype, namely the Geometric Template Matching, Artificial Neural Network and Cross Correlation. 26 alphabets from American Sign Language were chosen for training and testing the proposed prototype. The experimental results showed that Geometric Template Matching achieved the highest recognition accuracy compared to the other recognition techniques.