Users of compact smart products with small screens often have trouble learning the menu structure. If they cannot master the menu structure, users are not able to fully utilize the products. It is argued in this paper that using visual momentum in menu representation design helps users develop effective mental maps of menu structures and promotes learning of the user interface. To assess the effect of visual momentum in this study, four types of menu representations were developed. Additionally, two menu hierarchies, two types of function key layout, and two types of function key labeling were assessed to examine the effects of menu dimension and compatibility. Experimental results indicated that participants using a partial menu map with visual momentum design performed the best, and participants using a partial menu map without visual momentum performed the poorest, even worse than those-using command-only representation. The results also showed that the menu navigation problem appeared to be particularly significant with a deep menu hierarchy. Actual or potential applications of this research include menu representation design for compact smart products.

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PurposeExpert and novice readers tag documents with different descriptions; this study is intended to discover which readers would generate the most reliable and most representative sets of tags.Design/methodology/approachOne group of experts and one group of novices were recruited. These two groups were asked to provide tags for document bookmarks in a Mozilla Firefox browser. In the experimental analysis we defined two measures – similarity and relevance – to describe the differences between the two groups.FindingsTags chosen by experts yielded better similarity and relevance values in all analyses. Tags chosen by the expert group had higher commonality in pairwise similarity analysis; moreover, the relevance analysis showed that tags chosen by experts reflected better understanding of the content.Originality/valueTagging behavior has become highly popular on the web, and its study has commercial merit. Tags from experts represent the structure behind the knowledge involved; expert representation may be vastly more helpful than novice representation for promoting understanding of content in an era characterized by an explosion of information.

The accuracy and fluency of a handover task affects the work efficiency of human–robot collaboration. A precise and proactive estimation of handover time points by robots when handing over assembly parts to humans can minimize waiting times and maximize efficiency. This study investigated and compared the cycle time, waiting time, and operators’ subjective preference of a human–robot collaborative assembly task when three handover prediction models were applied: traditional method-time measurement (MTM), Kalman filter, and trigger sensor approaches. The scenarios of a general repetitive assembly task and repetitive assembly under a learning curve were investigated. The results revealed that both the Kalman filter prediction model and the trigger sensor method were superior to the MTM fixed-time model in both scenarios in terms of cycle time and subjective preference. The Kalman filter prediction model could adjust the handover timing according to the operator’s current speed and reduce the waiting time of the robot and operator, thereby improving the subjective preference of the operator. Moreover, the trigger sensor method’s inherent flexibility concerning random single interruptions on the operator’s side earned it the highest scores in the satisfaction assessment.

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