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The purpose of this paper is to map and describe findings from research conducted in workspaces designed to support activity-based working (ABW) over the past 10 years (2010–2020) with a view of informing post-COVID workplaces of the positive and negative attributes of ABW.

Scopus was used as the search engine for this review. Papers which reported findings related to ABW and performed field study in ABW workspaces with adult occupants were included. Out of the 442 initial papers, 40 papers were included following iterative title and abstract and full text review process and consideration of inclusion and exclusion criteria. These papers were divided into three groupings (organizational, human and physical environment) based on their major focus. Positive and negative effects of ABW environments on occupants are discussed within these three topics in consideration of the implications for the post-COVID workplace.

Although the included studies were inclined to be either more positive (i.e. interior design) or negative (i.e. indoor environmental quality, productivity, distraction and privacy) in relation to various attributes of ABW, no single effect of ABW environments on occupants was in full agreement between the studies. The shortcomings of ABW environments are more related to how this way of working is implemented and how occupants use it, rather than the concept itself. A partial uptake of ABW leads to occupants’ dissatisfaction, lower productivity and lower well-being, while a holistic approach increases the chance of success. It is hypothesised that many currently reported negative aspects of the ABW concept might diminish overtime as ABW evolves and as new challenges arise. A continuous post-occupancy evaluation after relocation to an ABW-supportive environment can inform the organization about the changing needs and preference of the occupants; hence, the organization can tailor the ABW solution to the arising needs. The inter-connection between the three key ABW pillars (organizational, human and physical environment) is crucial to the success of this concept specifically in the context of the post-COVID-19 workplace.

This paper highlights the key shortcomings and limitations of studies produced over the past decade and identifies keys gaps in the current body of literature. It provides a new insight on how findings related to open-plan offices designed to support ABW can be categorized on the three big heading of organizational, physical and human-related aspects, and further investigates the positive and negatives outcomes reported on ABW under these headings. It also discusses how the findings arising from this literature review can inform the post-COVID workplace.

In the rise of offices designed to support activity-based working (ABW), parts of industry have fully transitioned to open-plan environments and then later to unassigned seating, whereas other parts, such as tertiary education, are still in the process of moving away from individual offices. There are a few relevant studies to understand how occupants from industry sectors with different levels of adoption of ABW perceived environments designed to support this way of working. This paper aims to contribute to the knowledge gap by providing insight into workers’ satisfaction and dissatisfaction from open-plan offices designed to support ABW along with the key predictors of perceived productivity.

A data set of 2,090 post-occupancy evaluation surveys conducted in five sectors – tertiary education, finance, construction, property/asset management and design/engineering – was analyzed. ANOVA and confirmatory factor analysis (CFA) were conducted for the survey questionnaires. First, ANOVA tests were conducted for the whole sample with perceived productivity as the dependent variable. A seven-point Likert scale with five theoretical factors was generated with all survey questionnaires. CFA was performed to show the factor loadings. In addition, regression analyses were carried out for each of factor item taken as the independent variable, where perceived productivity was the dependent variable. Key sources of satisfaction and dissatisfaction per sector were analyzed and differences between occupants reporting a negative or positive impact on their productivity were also investigated. Finally, open-ended comments were analyzed to show the key sources of dissatisfaction based on open-ended comments.

Workers from construction were the most satisfied, followed by finance and tertiary education. Occupants from all industry sectors consistently rated their workspaces highly on biophilic and interior design. Distraction and privacy received the lowest scores from all sectors. Open-ended comments showed mismatches between spatial and behavioral dimensions of ABW both for satisfaction and perceived productivity. Interior design was the strongest predictor for perceived productivity for all sectors. Findings dispel the notion that ABW implementation may not be suitable for certain industries, as long as the three key pillars of ABW are fully implemented, including design, behavior and technology.

This paper provides insight into workers’ satisfaction and dissatisfaction from open-plan offices designed to support ABW in different industry sectors along with the key predictors of perceived productivity.

This study proposes a collaborative federated learning (CFL) framework to address personal data transmission and retention issues for artificial intelligence (AI)-enabled video surveillance in public spaces.

This study examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Based on the requirements, this study proposes a CFL framework to gradually adapt AI models’ knowledge while reducing personal data transmission and retention. The framework uses three different federated learning strategies to rapidly learn from different new data sources while minimizing personal data transmission and retention to a central machine.

The findings confirm that the proposed CFL framework can help minimize the use of personal data without compromising the AI model's performance. The gradual learning strategies help develop AI-enabled video surveillance that continuously adapts for long-term deployment in public spaces.

This study makes two specific contributions to advance the development of AI-enabled video surveillance in public spaces. First, it examines specific challenges for long-term people monitoring in public spaces and defines AI-enabled video surveillance requirements. Second, it proposes a CFL framework to minimize data transmission and retention for AI-enabled video surveillance. The study provides comprehensive experimental results to evaluate the effectiveness of the proposed framework in the context of facial expression recognition (FER) which involves large-scale datasets.

The paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces.

The paper proposes a new Responsible Artificial Intelligence Implementation Framework to guide the proposed solution's design and development. It defines responsible artificial intelligence criteria that the solution needs to meet and provides checklists to enforce the criteria throughout the process. To preserve data privacy, the proposed system incorporates a federated learning approach to allow computation performed on edge devices to limit sensitive and identifiable data movement and eliminate the dependency of cloud computing at a central server.

The proposed system is evaluated through a case study of monitoring social distancing at an airport. The results discuss how the system can fully address the case study's requirements in terms of its reliability, its usefulness when deployed to the airport's cameras, and its compliance with responsible artificial intelligence.

The paper makes three contributions. First, it proposes a real-time social distancing breach detection system on edge that extends from a combination of cutting-edge people detection and tracking algorithms to achieve robust performance. Second, it proposes a design approach to develop responsible artificial intelligence in video surveillance contexts. Third, it presents results and discussion from a comprehensive evaluation in the context of a case study at an airport to demonstrate the proposed system's robust performance and practical usefulness.

Affordable mobile devices with video playback functionality are rapidly growing in the market. Current wireless and third generation communication networks enable smoother and higher quality streaming video. With the support of these technologies, most participants in telecom value‐added service chain are planning to shift their business focus to a more profitable and appealing area, mobile TV. Previous work that survey on users' behavior while consuming mobile TV has indicated that users normally watch brief and casual contents, and not the full program. However, most of the current services adopt a “push” approach since users passively receive pre‐defined contents, rather than pulling the interesting topics and segments. In order to promote a more enjoyable and rewarding watching experience, this paper will propose a framework to support a fully interactive mobile TV. The main goal is to enable users to: 1) visually locate interesting topics across multiple genres (such as news, sports and entertainment) and 2) fully control the playback flow of the multimedia items while selecting the most interesting segments. A web‐based system has been developed to implement and test the effectiveness of the proposed framework in a wireless and mobile setting.