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The purpose of this study is to explore some quantitative indicators from the social network analysis, observe the variations of these indicators over time and determine whether these indicators fit into the traditional team development stages model or theory. The primary focus is on the perspective of social interaction rather than the suitability of the indicator, i.e. the authors will not determine the optimal indicators nor compare the performance of different indicators. This study aims to propose a quantitative method to identify the development stages of virtual teams.

Two phases were designed in this study. The first phase was a simple study to prove the preliminary ideas and explore which quantitative indicators would be suitable for analysis. In total, 16 undergraduates were randomly assigned to two virtual teams. They were required to complete an information system (IS) project through virtual teamwork and use information and communications technologies (ICTs) to communicate with each other. After proving the preliminary ideas, the authors collected communication data of the 30 virtual teams working on IS projects in the second phase. The total duration of this process was two months.

The findings practically identified three stages, which were found to be consistent with that of the previous qualitative studies. In the initial (inclusion) stage, intensity had an upward trend. In the second (control) stage, centralization had an upward trend. In the final (affection) stage, intensity and density had upward trends and centralization had a downward trend. Both density and centralization also became smooth in this final stage. The conclusion can serve as a basis for further studies in virtual team development.

A successful virtual team will help industries to reduce their costs and increase performance and benefits. The findings will help industries quickly and objectively identify which stage they are at. This quantified approach will provide managers and leaders with a simple, useful way to highlight the needs for managing different aspects of team behavior at each stage of development. After establishing this approach, managers and leaders will be able to make plans to improve existing processes, set priorities, provide suitable principles and guidelines, etc., and eventually improve virtual team performance.

Because of the popularity of digital cameras, the number of personal photographs is increasing rapidly. In general, people manage their photos by date, subject, participants, etc. for future browsing and searching. However, it is difficult and/or takes time to retrieve desired photos from a large number of photographs based on the general personal photo management strategy. In this paper the authors aim to propose a systematic solution to effectively organising and browsing personal photos.

In their system the authors apply the concept of content‐based image retrieval (CBIR) to automatically extract visual image features of personal photos. Then three well‐known clustering techniques – k‐means, self‐organising maps and fuzzy c‐means – are used to group personal photos. Finally, the clustering results are evaluated by human subjects in terms of retrieval effectiveness and efficiency.

Experimental results based on the dataset of 1,000 personal photos show that the k‐means clustering method outperforms self‐organising maps and fuzzy c‐means. That is, 12 subjects out of 30 preferred the clustering results of k‐means. In particular, most subjects agreed that larger numbers of clusters (e.g. 15 to 20) enabled more effective browsing of personal photos. For the efficiency evaluation, the clustering results using k‐means allowed subjects to search for relevant images in the least amount of time.

CBIR is applied in many areas, but very few related works focus on personal photo browsing and retrieval. This paper examines the applicability of using CBIR and clustering techniques for browsing personal photos. In addition, the evaluation based on the effectiveness and efficiency strategies ensures the reliability of our findings.