"Closing the Performance Gap"

We attempt to provide a novel antecedent of corporate social responsibility (CSR) by focusing on the role of CSR awards. Specifically, we investigate how competitors’ winning CSR awards incentivize non-winning firms’ CSR as a competitive catch-up. Using a difference-in-differences research design, we find that non-winners improve their CSR after their competitors have won CSR awards. Furthermore, based on the awareness-motivation-capability (AMC) framework from the competitive dynamics literature, we find that the media visibility of award winners, the performance gap of non-winners with award winners, and the prior CSR of non-winners strengthen the CSR competitive catch-up behaviors. Findings from this study contribute to the CSR research by highlighting the spillover effect of CSR awards as a meaningful event in incentivizing non-winning firms’ CSR and extending the AMC framework to explain the contingency factors of competitive catch-up in the context of CSR research.

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Managing energy performance in buildings from design to operation using modelling and calibration

Building Services Engineering Research and Technology ◽

10.1177/01436244211008317 ◽

2021 ◽

pp. 014362442110083

Author(s):

Nishesh Jain ◽

Esfand Burman ◽

Dejan Mumovic ◽

Mike Davies

Keyword(s):

Best Practice ◽

Good Practice ◽

Energy Performance ◽

Operating Conditions ◽

Design Stage ◽

Performance Gap ◽

Actual Performance ◽

Simulation Tools ◽

Calibration Protocol ◽

Covering Design

To manage the concerns regarding the energy performance gap in buildings, a structured and longitudinal performance assessment of buildings, covering design through to operation, is necessary. Modelling can form an integral part of this process by ensuring that a good practice design stage modelling is followed by an ongoing evaluation of operational stage performance using a robust calibration protocol. In this paper, we demonstrate, via a case study of an office building, how a good practice design stage model can be fine-tuned for operational stage using a new framework that helps validate the causes for deviations of actual performance from design intents. This paper maps the modelling based process of tracking building performance from design to operation, identifying the various types of performance gaps. Further, during the operational stage, the framework provides a systematic way to separate the effect of (i) operating conditions that are driven by the building’s actual function and occupancy as compared with the design assumptions, and (ii) the effect of potential technical issues that cause underperformance. As the identification of issues is based on energy modelling, the process requires use of advanced and well-documented simulation tools. The paper concludes with providing an outline of the software platform requirements needed to generate robust design models and their calibration for operational performance assessments. Practical application The paper’s findings are a useful guide for building industry professionals to manage the performance gap with appropriate accuracy through a robust methodology in an easy to use workflow. The methodological framework to analyse building energy performance in-use links best practice design stage modelling guidance with a robust operational stage investigation. It helps designers, contractors, building managers and other stakeholders with an understanding of procedures to follow to undertake an effective measurement and verification exercise.

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App2Vec: Context-Aware Application Usage Prediction

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3451396 ◽

2021 ◽

Vol 15 (6) ◽

pp. 1-21

Author(s):

Huandong Wang ◽

Yong Li ◽

Mu Du ◽

Zhenhui Li ◽

Depeng Jin

Keyword(s):

Dirichlet Process ◽

Service Providers ◽

State Of The Art ◽

Representation Learning ◽

Context Aware ◽

Challenging Problem ◽

Performance Gap ◽

Bayesian Mixture Model ◽

Bayesian Mixture ◽

Spatio Temporal

Both app developers and service providers have strong motivations to understand when and where certain apps are used by users. However, it has been a challenging problem due to the highly skewed and noisy app usage data. Moreover, apps are regarded as independent items in existing studies, which fail to capture the hidden semantics in app usage traces. In this article, we propose App2Vec, a powerful representation learning model to learn the semantic embedding of apps with the consideration of spatio-temporal context. Based on the obtained semantic embeddings, we develop a probabilistic model based on the Bayesian mixture model and Dirichlet process to capture when , where , and what semantics of apps are used to predict the future usage. We evaluate our model using two different app usage datasets, which involve over 1.7 million users and 2,000+ apps. Evaluation results show that our proposed App2Vec algorithm outperforms the state-of-the-art algorithms in app usage prediction with a performance gap of over 17.0%.

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