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In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research.

We propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice.

The literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples.

Based on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management.

This study aims to analyze the relationship between corporate social responsibility (CSR) and quality of accounting report, especially on earnings management (EM). In addition, potential moderators of this relationship are examined.

After a comprehensive study of potential mechanisms, the authors obtain plenty of empirical results to open the black box of the link between CSR and EM. Meta-analysis is applied on 51 studies from 35 papers. Further analysis is also carried out to determine the moderating effects, such as the cultural and sample selection differences in these papers.

CSR is negatively associated with EM. In addition, this effect is moderated by cultural difference, CSR measurement, and year of sample selection.

Two patterns of the hypothesis between CSR and EM are confirmed based on agency cost theory, a theoretical shift of corporate ethics based on organizational moral perspective. Several useful suggestions are also provided for future studies on the empirical model and sample selection. Further research is necessary to clarify the agency cost behind the two theoretical patterns.

CSR is not a tool for firms to market but rather a strategy to ensure their consistency with moral principles, indicating that management should pay more attention to the potential damage of the incongruence between CSR and accounting reporting quality. CSR reporting quality remains an important issue for legislature to guarantee continued firm operations.

To the best of the authors’ knowledge, this study is the first to analyze the CSR and EM link using a meta-analysis and to consider its underlying mechanism under the global environment. Previous method design and sample selection are reviewed to provide reference for future studies.

Recommending suitable doctors to patients on healthcare consultation platforms is important to both the patients and the platforms. Although doctor recommendation methods have been proposed, they failed to explain recommendations and address the data sparsity problem, i.e. most patients on the platforms are new and provide little information except disease descriptions. This research aims to develop an interpretable doctor recommendation method based on knowledge graph and interpretable deep learning techniques to fill the research gaps.

This research proposes an advanced doctor recommendation method that leverages a health knowledge graph to overcome the data sparsity problem and uses deep learning techniques to generate accurate and interpretable recommendations. The proposed method extracts interactive features from the knowledge graph to indicate implicit interactions between patients and doctors and identifies individual features that signal the doctors' service quality. Then, the authors feed the features into a deep neural network with layer-wise relevance propagation to generate readily usable and interpretable recommendation results.

The proposed method produces more accurate recommendations than diverse baseline methods and can provide interpretations for the recommendations.

This study proposes a novel doctor recommendation method. Experimental results demonstrate the effectiveness and robustness of the method in generating accurate and interpretable recommendations. The research provides a practical solution and some managerial implications to online platforms that confront information overload and transparency issues.