Context. Tailoring mechanisms allow performance dashboards to vary their appearance as a response to changing requirements (e.g., adapting to multiple users or multiple domains). Objective. We analyze existing research on tailored dashboards and investigate different proposed approaches. Methodology. We performed a systematic literature review. Our search processes yielded a total of 1,764 papers, out of which we screened 1,243 and ultimately used six for data collection. Results. Tailored dashboards, while being introduced almost thirty years ago, did not receive much research attention. However, the area is expanding in recent years and we observed common patterns in novel tailoring mechanisms. Since none of the existing solutions have been running for extended periods of time in real-world scenarios, this lack of empirical data is a likely cause of vaguely described research designs and important practical issues being overlooked. Implications. Based on our findings we propose types of tailoring mechanisms taking into account the timing and nature of recommendations. This classification is grounded in empirical data and serves as a step ahead to a more unifying way of looking at tailoring capabilities in the context of dashboards. Finally, we outline a set of recommendations for future research, as well as a series of steps to follow to make studies more attractive to practitioners.
Kruglov A, Strugar D, Succi G (2021). Tailored performance dashboards—an evaluation of the state of the art. PEERJ. COMPUTER SCIENCE., 7, 1-25 [10.7717/peerj-cs.625].
Tailored performance dashboards—an evaluation of the state of the art
Succi G
2021
Abstract
Context. Tailoring mechanisms allow performance dashboards to vary their appearance as a response to changing requirements (e.g., adapting to multiple users or multiple domains). Objective. We analyze existing research on tailored dashboards and investigate different proposed approaches. Methodology. We performed a systematic literature review. Our search processes yielded a total of 1,764 papers, out of which we screened 1,243 and ultimately used six for data collection. Results. Tailored dashboards, while being introduced almost thirty years ago, did not receive much research attention. However, the area is expanding in recent years and we observed common patterns in novel tailoring mechanisms. Since none of the existing solutions have been running for extended periods of time in real-world scenarios, this lack of empirical data is a likely cause of vaguely described research designs and important practical issues being overlooked. Implications. Based on our findings we propose types of tailoring mechanisms taking into account the timing and nature of recommendations. This classification is grounded in empirical data and serves as a step ahead to a more unifying way of looking at tailoring capabilities in the context of dashboards. Finally, we outline a set of recommendations for future research, as well as a series of steps to follow to make studies more attractive to practitioners.File | Dimensione | Formato | |
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