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Improving construction projects' performance through innovative approaches such as lean construction (LC) and offsite construction (OSC) methods are at the centre of various debates. However, there is a limited understanding of the current link between LC and OSC approaches. This study aims to conduct a scientometric analysis on LC and OSC research to unpack and establish the nexus and suggest future research focus.

Scientometric analysis was used to systematically examine existing literature on LC and OSC to identify possible connections. Relevant publications were extracted from the Scopus database, using inclusion and exclusion criteria. VOSviewer software was used as a visualisation technique to analyse and map the interrelations and connections of the concepts being studied. Bibliograhic data on the 68 selected papers were extracted from the Scopus database.

The search results cover the period between 2003 and 2021. Descriptive statistics show that the number of published papers has increased yearly. Researchers in the USA and Canada are the most productive authors regarding the number of published papers. The directions for future research suggested are the need to identify best practices for integrating LC and OSC methods, the need for more interdisciplinary and cross-country collaboration among researchers, the use of alternative research methods will provide a better understanding of the benefit of integrating LC and OSC techniques and more research is needed to showcase how the use of lean and offsite construction can facilitate the attainment of net-zero in the construction industry.

This study provides insights into the trends and gaps in knowledge on integrating LC and OSC methods and offers valuable insights to scholars and practitioners in integrating LC and OSC principles. This knowledge is vital for identifying strategies to improve the outcome of construction projects and contribute to the sustainable socio-economic development of cities across the globe.

Autonomous vehicles (AVs) have the potential to transform the infrastructure, mobility and social well-being paradigms in New Zealand (NZ) amid its unprecedented population and road safety challenges. But, public acceptance, co-evolution of regulations and AV technology based on interpersonal and institutional trust perspectives pose significant challenges. Previous theories and models need to be more comprehensive to address trust influencing autonomous driving (AD) factors in natural settings. Therefore, this study aims to find key AD factors corresponding to the chain of human-machine interaction (HMI) events happening in real time and formulate a guiding framework for the successful deployment of AVs in NZ.

This study utilized a comprehensive literature review complemented by an AV users’ study with 15 participants. AV driving sprints were conducted on low, medium and high-density roads in Auckland, followed by 15 ideation workshops to gather data about the users’ observations, feelings and attitudes towards the AVs during HMI.

This research study determined nine essential trust-influencing AD determinants in HMI and legal readiness domains. These AD determinants were analyzed, corresponding to eight AV events in three phases. Subsequently, a guiding framework was developed based on these factors, i.e. human-machine interaction autonomous driving events relationship identification framework (HMI-ADERIF) for the deployment of AVs in New Zealand.

This study was conducted only in specific Auckland areas.

This study is significant for advanced design research and provides valuable insights, guidelines and deployment pathways for designers, practitioners and regulators when developing HMI Systems for AD vehicles.

This study is the first-ever AV user study in New Zealand in live traffic conditions. This user study also claimed its novelty due to AV trials in congested and fast-moving traffic on the four-lane motorway in New Zealand. Previously, none of the studies conducted AV user study on SUV BMW vehicle and motorway in real-time traffic conditions; all operations were completely autonomous without any input from the driver. Thus, it explored the essential autonomous driving (AD) trust influencing variables in human factors and legal readiness domains. This research is also unique in identifying critical AD determinants that affect the user trust, acceptance and adoption of AVs in New Zealand by bridging the socio-technical gap with futuristic research insights.