1995

He (1995)

He put   forward an important conclusion that, the safety information highway is the future   direction of safety management science.

2005

Wang and Wang (2005)

They   discussed the status quo and outlook of intelligence and security   informatics. In their study, safety informatics research in some safety fields   (such as public safety) were mentioned.

2007

Li and Chen (2007)

They   discussed the importance, connotation, extension, and system of safety   management information system discipline.

2012

Zhang and Zhu (2012)

Safety   information engineering is a branch of safety informatics. They pointed out   that safety information engineering is a vital professional course for safety   technology and engineering majors, and proposed a framework for the safety   information engineering course.

2015

Huang et al. (2015)

They proposed   six core principles of safety informatics, namely safety information quality   principle, safety information grade principle, safety information sharing   principle, safety information prevention, and control principle, safety   information feedback principle, and safety information supervision principle.

2017

Luo and Wu (2017)

They put   forward the research methodology of safety informatics, which includes some   specific research methods of safety informatics (e.g., the collection method,   the summary method, the statistic method, etc.), and the general procedure   for safety informatics research.

2018

Wang and Wu (2018a)

They analyzed   reasons for the establishment of safety informatics. According to their   analysis, the establishment of safety informatics has a profound theoretical   and practical basis. Meanwhile, they deeply explored the   ten basic disciplinary issues of safety informatics (including its definition,   connotation, attribute, foundation, research object, etc.).

1

Safety information cognition model

•     Leiter and Rheinberger (2016)   developed a model of how athletes engaged in risky sports value safety   information;

•     Wu (2017) constructed a general safety information cognition   model to reveal the mechanism and fault mode of safety information   transmission in complex systems;

•     Luo and Wu (2018) proposed a safety information cognition approach   to reveal the accident mechanism, and

•     Lei Yu et al. (2018) developed a safety information cognitive   process model.

2

Safety information flow model

•     Westrum (2014) summed   up his research and understanding of safety information flow. We think that his   opinions on the safety information flow can provide an important insight into   research on the safety information flow model;

•     Wang and Wu (2018a) suggested   that, in a system, the safety material flow, the safety energy flow, and the   safety behavior flow can be unified into the safety information flow, and

•     Wu and Huang (2019) built   a system safety information flow structure model.

3

Safety information supply model

•     Many researches (e.g., Drupsteen and Boustras, 2016; Ferguson et al., 2011)   pointed out that effective safety information supply is essential to ensure safety;

•     Wang and Wu (2018b) proposed   a theoretical model for safety information supply, and

•     Shang (2017)   discussed a public safety information sharing model, and

•     Kaza and Chen (2008) surveyed   public safety information sharing initiatives.

4

Safety information literacy (including safety   data literacy) model

•     Wang and Wu (2018c) proposed   the concept of safety information literacy for the first time, and   constructed a safety information literacy model (according to their model, safety   information literacy mainly includes four aspects, namely, safety information   demand consciousness, safety information acquisition ability, safety   information evaluation ability, and safety information utilization ability);

•     Yang (2012) explored   and constructed a model for information literacy of   safety professionals in safety management, and

•     Wang et al. (2019b)   presented a theoretical framework for data literacy for safety professionals   in safety management.

5

Safety information behavior model

•     Wang and Wu (2018d)   proposed the concept of safety information behavior for the first time,   and constructed a meta model for safety information behavior which explained   the mechanism of safety information behavior, that is ‘safety information   need→safety information demand→safety information motivation→safety   information seeking behavior→safety information utilization behavior’, and

•     Huang et al. (2017a) constructed   a conceptual model of individual safety information capacity.

6

Safety information communication, diffusion or   transmission model

•     Wincek (2011)   put forth a concise communication method for critical process safety   information;

•     Xia (2015) built   a quality and safety information diffusion model, and

•     Many scholars (e.g., Xiao,   2018; Zhou et al., 2008)   developed product or food safety information transmission models.

7

Safety information collection and analysis   model

•     The Merseyside Accident Information Model (Davies and Manning, 1994a, Davies and Manning, 1994b)   can collect all available accident data without writing, typing or coding,   and accident data are immediately available for analysis.

8

Safety information feedback model

•     Wu (1983) built   a safety information feedback model.

Individual

level

1

Surry (1969)

1969

Surry model

The error   in human information processing can cause dangers.

2

Cui (1995)

1970

Hale model

An   accident occurs when humans do not respond appropriately to the actual   situation of the event (namely, safety information).

3

Qin and Peng (2005)

1972

Wigglesworth model

Various   information constantly acts on people’s senses, stimulating them. If people   react appropriately to the stimulus, an accident will not occur. On the   contrary, if people react to the stimulus incorrectly or improperly, risks   may occur, which may cause accidents.

4

Lawrence (1974)

1974

Lawrence model

Human   error is the cause of accidents in gold mining. The most dominant of human   errors are failures to perceive warnings (namely, warning information) of   danger.

5

Qin and Peng (2005)

1978

Anderson model

Based on   the extension and amendment of the Surry model, the Anderson model was   proposed. It is similar to the Surry model.

6

Rasmussen (1987), and Katsakiori et al. (2009)

1987

SRK framework

SRK (Skill-, Rule-, and Knowledge-based behavior) framework   distinguishes between three different levels of human cognitive control of   the environment.

7

Hale and Glendon (1987),   as well as Lacroix and Dejoy (1989)

1987

Hale and Glendon model

Hale and   Glendon’s model is concerned with safety perceptions and   decisions, and is based on the attribution theory which focuses on how people   process information in determining the causality of events.

8

Hollnagel (1998) and Katsakiori et al. (2009)

1998

CREAM

CREAM (Cognitive Reliability and Error Analysis Method) describes   the full context in which errors and accidents occur.

Organizational(systemic) level

9

Leveson (2004)

2004

STAMP model

STAMP   (System-Theoretic Accident Model and Process) regards systems as interrelated   components that are kept in a state of dynamic equilibrium by feedback loops   of information and control.

10

Zhao and Zhou (2012)

2012

Accident causation model based on safety   information missing

Safety   information is an informational expression of various factors of accidents.   The lack of safety information is the main potential cause of accidents.

11

Li et al. (2017)

2017

Multilevel safety information asymmetry model

Information   asymmetry is the main cause of accidents in the organization.

12

Wang and Wu (2018e)

2018

FDA accident model

FDA   (Forecast-Decision-Action) accident model points out that, in the absence of   safety spoofing, the lack of safety information is the main cause of failures   in safety forecast, safety decision-making, and safety action.

13

Wu and Huang (2019)

2019

Accident model based on information flow

The role   of information flow in accident causation is profound. The breakdown of   information flow (such as the failure of information acquisition) can potentially   cause an accident.

1

Safety   decision-making

•     Huang et al. (2018b) developed a   conceptual framework for big data-driven safety decision-making, and analyzed   the influencing factors of safety decision-making based on big data, and

•       To use data-driven safety   decision-making to realize smart safety management in the era of big data, Wang et al. (2019c) stated the definition,   benefits, theoretical foundations, fundamental elements, and influencing   factors of data-driven safety decision-making.

2

Safety monitoring

•       Shi and Abdel-Aty (2015) discussed the   application of big data in urban highway safety monitoring, and

•     Bychkov et al. (2016) discussed the   application of big data in ground-based safety monitoring.

3

Accident   investigation and analysis

•     Huang et al. (2017b) proposed a new paradigm for   accident investigation and analysis in the era of big data, and

•     Huang and Zhou (2019) developed a mine accident   prediction and analysis approach based on multimedia big data.

4

Safety risk   prevention and control

•     Walker and Strathie (2016) suggested that the big data and   ergonomics methods is a new paradigm for tackling strategic transport safety   risks, and

•       Cao et al. (2017) and Wang et al. (2018) discussed safety risk early-warning   and governance based on big data.

5

Emergency   management

•     Ragini et al. (2018) highlighted that the real-time   categorization and classification of social media big data can ensure   effective disaster response and recovery,

•     Guo and Liu (2016) conducted a study on emergency   data quality governance in the era of big data, and

•       Many other studies (e.g., Pang, 2015; Ma and Mao, 2015; Wu, 2017) explored the application of big   data in emergency management.

1

Safety monitoring

GIS, GPS, sensor technology,   database technology, network technology, communication technology, etc.

2

Accident (disaster)   prevention and control

Computer simulation   technology, database technology, virtual reality technology, image   measurement technology, big data technology, etc.

3

Safety risk   management

Machine learning   technology, big data technology, assisted decision-making support system, artificial   neural network technology, etc.

4

Safety education and   training

Multimedia   technology, computer network technology, animation technology, virtual   reality technology, visualization technology, etc.

5

Emergency management

Internet of things   technology, network technology, assisted decision-making support system,   visualization technology, database technology, artificial intelligence   technology, etc.