Identifying Demographic, Social and Professional Characteristics for Effective Disaster Risk Management—A Case Study of the Kingdom of Saudi Arabia

Abstract

The aim of this study is to address a gap in the literature associated with the influence of demographic characteristics of personnel working in disaster risk management on the organisational level of preparedness in this field. The study further aims to identify the impact of human, organisational, technological, coordination and environmental factors on the level of readiness in Saudi Arabia in dealing with crises and disasters. The case study applied a purposeful sampling approach in collecting 550 questionnaires from representatives of five geographical regions, 20 government organisations comprising 13 administrative regions. The study tested two hypotheses with the single-variance analysis test (P) performed for each stage (level) of the readiness of the relevant government departments inclusive of the demographics—age, education, position/job title, academic specialisation, number of disaster risk management related short courses completed and residential region of the study members. The findings suggest the influence of disaster management short course education and the region in which the respondent is located impacted significantly on the level of crisis and disaster organisational preparedness. Lesser impact on level of readiness for dealing with crises and disasters was identified for demographics of age, education level, position held and academic specialisation. Further, in the second area of the study findings indicate minimal variation in the impact of human, organisational, technological, coordination and environmental factors on the readiness of government departments in all phases of disaster risk management with all factors trending neutral and consistent with the weighed response averages.

1. Introduction

Despite numerous preventative and response mechanisms undertaken, the direct or indirect effects of natural hazards including loss of lives and financial burdens are on the rise. Effective natural hazard minimization/elimination requires disaster risk management that entails organized efforts of administrative and operational skills and capacities to minimize the negative consequences of hazards through implementing appropriate strategies and policies [1]. Unlike relief and reconstruction following disaster occurrence, disaster risk management (DRM) necessitates placing efforts in four phases—mitigation, preparedness, response, and recovery. It is less clear which characteristics of disaster management, i.e., professional, organisational, environment and technical factors, play the more significant roles for successful disaster risk management. This research attempts to contribute to this gap of knowledge by analysing the perspective of government representatives, DRM professionals and their respective demographics, education level, academic specialisation, number of short courses completed on DRM, location/environment and position held in their organisation in the Kingdom of Saudi Arabia (KSA). Understanding the level of preparedness and the factors that influence the level of preparedness for DRM contributes to developing strategies, policies and procedures for enhancing preparedness levels and reducing the level of consequences, including reducing human and monetary losses.

To place the following literature review, study methodology, discussion and conclusions into context, we have considered definitions used by the United Nations General Assembly for terminology relating to disaster risk reduction [2], p. 10–24. Disaster is defined as ‘a serious disruption of the functioning of a community or a society at any scale due to hazardous events interacting with conditions of exposure, vulnerability and capacity, leading to one or more of the following: human, material, economic and environmental losses and impacts.’ Emergency and emergency management is also used, sometimes interchangeably, with the term disaster management, particularly in the context of biological and technological hazards and for health emergencies. Disaster risk management (DRM) is the application of disaster risk reduction policies and strategies to prevent new disaster risks, reduce existing disaster risk and manage residual risk, contributing to the strengthening of resilience and reduction of disaster losses.

The overall purpose of this paper is to present a study exploring the influence of personnel demographic characteristics on the level of effective disaster risk management.

2. Background

Saudi Arabia located in the west of the Asian continent with an area of 2,146,690 km² has experienced both natural hazards and man-made disasters (

Table 1. The list of major natural hazards and man-made disasters (excluding bombing, communal violence and explosion) between 1963 and 2022.

Natural Hazards			Man-Made Disasters
Year	Name of Hazard	Effects	Year	Name of Disaster	Effects
1964	Floods	20 deaths and 1000 injuries, homelessness, and food insecurity	1975	Fire	200 deaths and numerous injuries
1985	Floods	32 deaths plus another 5000 people affected	1990	Stampede in a tunnel	1426 deaths
2000	Disease outbreak	Rift Valley Fever outbreak killed 133 and infected 500 people	1994	The Hajj stampede	270 deaths
2001	Disease outbreak	Killed 35	1997	Fires	343 deaths and 1500 casualties
2003	Floods	12 deaths plus another 50 people affected	1998	Stampede	118 deaths, 180 injuries
2009	Floods	125 deaths, more than 10,000 people affected	2004	The Mina valley stampede	251 deaths
2011	Floods	11 deaths, with a total destruction cost of USD 300 million	2006	The annual Makkah pilgrimage stampede	380 deaths and 280 injuries
2013	Floods	24 deaths, plus another 900 people affected	2015	The Mina Valley stampede	769 deaths and 934 injuries
2019	Floods	7 deaths, 11 injured and 1111 affected	2015	Hospital fire in intensive care and maternity wards	25 deaths and 123 injuries
2020–2022	Coronavirus (Covid-19)	cumulative cases 805,879 cumulative deaths 9233 World Health Organisation 2022	2017	Fire in an industry	10 Deaths

). The major natural hazards include floods and disease, and man-made disasters include stampedes, fires, motor vehicle accidents, explosions and communal violence. The International Disaster Database at the Centre for Research on the Epidemiology of Disasters maintains a worldwide emergency event database commencing from 1900 with the first record of data for the KSA available from 1964 [3]. A total of 21 emergency events reported in the database caused deaths and injuries with the total number of reported deaths from such events ranging from 10 to 9233. The 9233 COVID-19 deaths were reported by the World Health Organisation live statistic as of 22 July 2022 [4]. As indicated in Table 1 in terms of reported deaths, with the exception of the 2020–2022 COVID-19 pandemic, stampedes resulting from pilgrimages are the most severe emergency events. For example, in a single year (2015), such an event caused 2236 deaths. Table 1 presents the list of major natural hazards and man-made disasters (excluding bombing, communal violence and explosion) between 1963 and 2022.

Research Gap

This study focuses on exploring the influence of social and professional characteristics on the of level of preparedness for managing crises and disasters by government employees in KSA, to (a) contribute to developing strategies for enhancing the level of preparedness for KSA and the global risk management community and (b) by association, contribute to closing a gap in the literature associated with understanding the influence of employee demographics on the level of organisational preparedness. The second area of contribution is as a baseline study for understanding from the perspective of those working in disaster risk management, the respective influence of human, technological, environmental and organisational factors on the level of overall organisational preparedness to face disasters, crises and emergencies.

3. Literature Review

There continues a developing body of literature associated with understanding what attributes and factors lead to a community’s risk perception, capacity and capability for crises and disaster preparedness [5,6,7,8] As explained by Oh & Lee [9] as a result of their systematic review of emergency management research that

…topics of communication, collaboration, citizen participation, risk perception, and vulnerability have been addressed consistently over the last four decades. …scholars have responded to emerging challenges by establishing emergency management systems, designing an international framework for climate change, and shifting focus from response to mitigation with special emphasis on community resilience and sustainability.

Studies focused on organisation resilience are plentiful, with diverse specific topics associated with implementing a response to disasters and developing and sustaining organisational resilience. The work of Andersson [10] examples the research and publication focus on organisational traits for building organisational resilience. The work concluding risk awareness is the basic trait for organisational resilience. In a similar trend, Rai [11] investigated the influences on organisational resilience and social-economic sustainability post-COVID-19 in the context of DRM, concluding that predicting the crisis and disruptions, building robustness and recoverability have a positive effect on both the social and economic aspects of sustainability. Developing an understanding of social resilience, i.e., community resilience, in the context of disaster preparedness as presented, for example, in the work of Imperiale & Vanclay [12], Saja [13] and Obrenovic [14] is an increasing body of literature, a significant catalyst being the research associated with the response to the global coronavirus pandemic. The findings of the Zhong [15] study (a systematic review of literature associated with the impact of COVID-19) indicates that human resource practices have made an increased contribution to the performance of organisations in this field. The literature associated with identifying the influence of individual social and professional characteristics on an organisation’s capability and capacity in the four stages of risk management are limited. The work of Drzewiecki [16] for example, analysed the association between educational attainment and resilience (the study adjusted for demographics of education, gender, race, average monthly income and island of residence) concluding those with a higher level of education were significantly more likely to be resilient to natural hazard-induced disaster. An exploratory study by Baker [17] of factors affecting the level of perceived competence in disaster preparedness among nurses based on their personal and work-related characteristics concluded that years of experience were perceived to increase competence in disaster preparedness. The work of Nikkanen [18] explored the influence of socio-economic factors on community members’ preparedness for natural hazards (extreme storm events in Finland.) A focus of the work of Nikkanen [18] was to understand the influence of age, previous experience, place and type of residence and socio-economic vulnerability on the respective level of preparedness for disasters. The authors concluded these factors had marginal influence on participants’ approach to preparedness.

The point of difference between the Nikkanen [18] study and the KSA study is the characteristics of the sample, i.e., the KSA sample are employees within government departments of the civil defence responsible for disaster risk management, whilst the Finnish study included members of the general community, ‘targeted to people living in Finland’ [18] (para. 23). Bronfman [19] studied the level of preparedness of a sample of community members who had experienced two natural hazards (earthquakes and flood) to identify the primary sociodemographic characteristics aligned to level of preparedness. In the context of the KSA study, the demographic of age included in the Bronfman study offers a level of comparison, the Bronfman study concluding the subject’s age is significantly related to their declared levels of preparedness: in general, subjects of 30 to 59 years of age declared the highest levels of preparedness [19] (para. 50).

A valuable work by Young [20] explored the positive outcomes of organisational resilience to identify how resilient employees through adapting and initiating changes are able to support organisational resilience following a crisis. The work concludes that employee intentions for proficiency, adaptivity and proactivity contributes to organisational effectiveness after a crisis situation.

The earlier work of Wachinger [21] explored cultural and individual factors including age, gender, education, social and economic status of the residents in a series of case studies from across the globe to understand what influences risk perception and preparedness [21]. The study concluded that a stronger connection between experience, trust, perception and preparedness to take protective actions was widely observed [21]. Himes-Cornell [22] suggests communities with strong political, social and financial capitals tended to fare better immediately following disasters, enabling longer-term processes of transformation or recovery. Of note, the recent work of Naheed [23] suggests that the way forward to develop sustainably in the current climate (i.e., in the 2020 years) is to integrate disaster risk reduction into investments decisions, whereas in this work the demographic characteristics of those working in the disaster reduction and management field were not included.

The KSA study is unique as it centres on understanding individual characteristics of age, position, location, qualifications, number of disaster risk management associated short courses completed and academic specialisation of personnel working in key areas of DRM on an organisational level of preparedness in the four stages of DRM.

In general, across the globe, it is the local institutions who are the first to respond to any kind of disaster and emergency events [24,25]. Although the roles of local institutions in disaster management are well studied in developed countries [26], they are understudied in many developing countries [27]. There are ongoing emphases on the importance of institutions [28] because they play the most vital role in DRR activities implemented at the community level [29]. The lack of capacity of local institutions, coordination and cooperation among local, regional and national government authorities may exacerbate a community’s vulnerability to disaster [28]. The work of Alam and Ray-Bennett [24] identified that risk management functions well during the response, evacuation, rescue and relief phases compared to the preparedness phase, where the level of government disaster preparedness affects public preparedness, especially when subjected to real disasters and witnessing the actual consequences of disasters. In the process of understanding influences on aspects related to disaster preparedness and response, there remains scope for identifying the impact of human, organisational, technological, coordination and environmental factors on the level of preparedness in each phase of DRM. Whilst acknowledging the wealth of literature associated with disaster risk, there is limited literature which expressly investigates the perspective of those working in governance roles for DRM on how they perceive the relative influence levels of human, organisational, technological, coordination and environmental factors on the level of preparedness in each phase of DRM. In a similar trend as identified for literature associated with individual demographic characteristics as discussed above, of the literature related to the factors influencing organisational preparedness there is a focus on governance elements, as exampled by the work of [30]. The work of Raikes [30] in linking disaster risk reduction and human development recommended disaster risk governance approaches should reflect the capacities and needs of individuals and vulnerable populations.

The work of Bracci [31]) similarly suggests, following their structured literature review of risk management in the public sector, there remains gaps in the knowledge of risk management in the public sector. The work proffers four main areas for future developments to increase the body of knowledge including risk management and managerial systems. A review of DRR literature in the period 1990–2019 by Orimoloye [32] suggests the published research on DRR-related hotspots is focused primarily on disaster management and science, environmental science, climate change and ecosystem services (p. 637). Cognizant of the limited published research focused on understanding from the perspective of those working in disaster risk management, and the respective influence of human, technological, environmental and organisational factors on the level of overall organisational preparedness to face disasters, crises and emergencies, the KSA case offers a base line study contribution to the body of knowledge.

The Present Institutional Framework of Disaster Management in Saudi Arabia

The development of emergency management plans in Saudi Arabia began more than 80-years-ago with the formation of the fire brigade in Makkah in 1927 [33]. In 1948, the Makkah Fire Brigade joined the later-established Centre of General Security to form the General Security and Fire Services. In 1965, the General Directorate of Civil Defence (GDCD) was formed as a follow-up of recommendations by the International Association of Fire Fighters. The scope of the GDCD was wider than of its predecessors by undertaking civilian defence activities in both normal and crisis periods. As availability of funds and telecommunication capacity developed, the GDCD began expanding to both urban and rural areas of the Kingdom. As a result of failing to reduce stampedes to an accepted level, King Fahad ordered a reform of the GDCD’s structure and strategic goals and obligations in 1987. The reform sought to receive experiences from the other countries by visiting their civil defence management. The current structure of the GDCD is divided into three levels: Board of GDCD, Executive Committee, and volunteers.

The present study offers an opportunity to extend the knowledge associated with understanding the influence of additional demographic factors not yet reported in the literature and to offer further insight into the respective influence of key factors in DRM.

4. Methodology, Data Collection and Analysis

The context of this study aligns in the main with the case study approach as discussed by Yin [34]), Merriam and Tisdell [35], and Punch [36]. The seminal work in the field of case study research as discussed by Yin [34] suggests a case study is an investigation of a phenomenon in a real-life context. The characteristics of a case study approach are further explained by Punch [36] and Merriam and Tisdell [35] in suggesting that case studies are employed when there is a particular group or situation/circumstance at the centre of the study and there is a perimeter or boundary around this centre. Case study theory suggests the phenomenon under study has boundaries, Merriam and Tisdell [35] suggest it is a ‘bounded system’ (p. 41). In the context of this study, the boundaries are defined by the participants and the circumstances, i.e., working in the field of DRR in KSA. The study objectives are designed to enable relationships between demographics and DRR management effectiveness to emerge. New knowledge revealed in this manner is consistent with the seminal work of Stake [37] in defining case studies as research designed to allow ‘previously unknown relationships … to emerge (p. 47). Selection of the data collection method and data analysis in case study research varies, a common theme indicates the selection is determined by the researcher based on the most appropriate tools to capture the requisite data and subsequent analysis [34,38].

Following a review of the literature of disasters and crisis management and based on the objectives of the study, a questionnaire with 35 questions was designed to measure the variables for the study in Supplementary Materials S1. In the questionnaire, 5 indicators are dedicated to humanitarian factors, 6 dedicated to organisational, 5 dedicated to environmental, 3 dedicated to technological and 10 dedicated to measure the impact of demographic factors on readiness.

Two hypotheses were developed for the study that informed the study methodology:

Hypothesis 1.

There are no statistically significant differences in the readiness levels of the relevant government departments due to the demographic characteristics of age, academic qualification, job title, academic specialisation, number of courses in disaster management, the region to which the organisation belongs.

Hypothesis 2.

There is no statistically significant effect on the human, organisational, technological, coordination, environmental factors individually and collectively in the readiness levels of government departments concerned in the four stages of disaster management.

4.1. Variables Used in the Present Study

Based on the research objectives of the study, a model has been developed showing the assumed relationship between the independent and dependent variables of this study. Figure 1 shows the variables that are considered in the present study. There are five independent variables used in this study. First, staff demographics which include age, academic qualification, career level, academic specialisation and the number of courses a staff enrolled in within the field of functional specialisation. Second, organisational factors including leadership style, objectives and policies of the organization, the extent to which strategies exist and the material resources available to support it. Third, technological factors that include communication systems for information handling in crisis situations, mechanisms and equipment in the organisation, and the technological capabilities available to them. Fourth, environmental factors that include population density, population culture and weather conditions. The fifth variable includes coordination factors which refer to the level of coordination between officials of the concerned government agencies in their readiness to face crises.

The present study has utilized four dependent variables which are readiness in mitigation, preparedness, response and the disaster/crisis recovery phase. The variables considered for mitigation are measures aimed at removing the causes of the crisis and reducing their likelihood of occurrence and their impact on humans and the environment. The variables included in the preparedness phase are actions aimed at protecting lives and property from the effects of risks that cannot be fully prevented through mitigation measures. The response phase includes the prediction of the occurrence of danger and ending with the stability of the situation after the end of the danger. The ability to predict the occurrence of danger varies according to the type of danger. Finally, the recovery phase includes short-term provision of assistance and rehabilitation, and long-term including reconstruction and assistance, as well as maximizing lessons learned from the respective disaster and previous disasters.

4.2. Brief Description, Population, Sample Size and Questionnaire Design of the Study Area

In the present study, only five regions are selected, based on the characteristics of the population to represent all regions of Saudi Arabia—the regions of: North, Eastern Province Sharq, Jazan area, South of the Al-Madinah Al-Munawwarah area and the Riyadh Region.

The civil defence council consists of 20 organizations in each region in the Kingdom of Saudi Arabia. Thirteen government organizations representing the majority and most important members of the civil defence committees have been selected for this study based on considering geographical differences, geographical distribution, climatic variations, administrative differences, differences in vulnerability, differences in the quality of hazards and the differences in degree of vulnerability. This selection enabled representation of the diversity in these factors across the Kingdom. The selected organisations are as follows: the regional police; the regional secretariat (the Ministry of Municipal and Rural Affairs); the General Directorate of the Ministry of Finance branch; Ministry of Transport in the Region; General Directorate of the Ministry of Water and Electricity; Electricity Sector in the Region; General Directorate of the Ministry of Water and Electricity; Water Sector in the Region; General Directorate of the Ministry of Social Affairs Branch; General Branch of the Ministry of Education in the region; the General Administration of the branch of the Red Crescent Authority in the region; the traffic of the district administration; the General Administration of Meteorology and Environmental Protection in the region.

In total, 550 surveys have been collected for the study area, with 110 collected from each region. The population of this study consists of all 110 employees in the civil defence bodies/agencies that have specific duties as members of the civil defence committees within the five representative regions.

4.3. Process of the Data Quality Control

To control the quality of the collected data, at first, the questionnaire was presented to 19 faculty members from King Abdelaziz University, the College of Business Administration in Jeddah, An-Najah National University, The College of New York and the Arab Academy for Science, Technology and Maritime Transport. Faculty members were asked to express their opinion on the inclusion of the questions in the survey lists, the relevance of the indicators, consideration of the adequacy of the tool in terms of the number of phases and their comprehensiveness and coverage of all dimensions of the study, with respect to modification, deletion or addition. Finally, the questionnaire formed by the recommendations of the faculty members was tested in the field by piloting to check the consistency and relevancy of the questions. Following feedback piloting, the questionnaire was finalized (no changes were recommended from the pilot study).

4.4. Reliability Assessment Using Statistics

To test the stability of the questionnaire used in data collection, and the extent to which the components are related to each other, to ensure that they do not overlap, the coefficient of Cronbach’s alpha was used. Cronbach’s alpha α is a measure of internal consistency, that is, how closely related a set of items are as a group. Schanze [39] argues that Cronbach’s alpha is not a statistical test—it is a coefficient of reliability or consistency. Cronbach’s alpha takes values ranging from zero to the correct one. If there is no consistency in the data, the value of the parameter is equal to zero. Equally, if there is complete consistency in the data, the value of the parameter is equal to the correct one. In other words, increasing the value of Cronbach’s alpha coefficient means increasing the stability of the questionnaire list. The results of calculating the coefficient of Cronbach’s alpha are shown in

Table 2. Results of the stability test for the elements of the dependent variable and the independent variable.

Name of the Variables	Crisis Management Stages (Dependent Variable) ↓	Cronbach’s Alpha Coefficient
First: elements of the dependent variable	Mitigation Phase	0.827
	Preparedness Phase	0.919
	Response Phase	0.843
	Recovery Phase	0.905
	Level of readiness	0.958
Second: elements of the independent variable	Humanitarian factors	0.656
	Organisational factors	0.775
	Technological factors	0.858
	Environmental factors surrounding	0.239
	Coordination factors	0.312
	Potential influencing factors in readiness ↑	0.855

. As illustrated in Table 2, all the variables are aligned with each other, thus no inconsistency is present in the questionnaire, indicating a reliable questionnaire for primary collecting data.

5. Results

In this research study, the first hypothesis states that “there are no statistically significant differences in the readiness levels of the relevant government departments due to the demographic characteristics of age, academic qualification, job title, academic specialisation, number of courses in disaster management, in the region to which the organisation belongs”. To test this hypothesis, the single-variance analysis test (P) was performed for each stage (level) of the readiness of the relevant government departments with each characteristic of the demographics.

Table 3. Results of F-test between readiness levels and sample ages.

Phase	Age	No.	Weighted Average	Standard Deviation	F Value	Significance Level	Statistical Significance
Mitigation	<30 years	49	3.33	0.96	1.411	0.239	Not Valid
	From 31 to 40 years	155	3.40	0.85
	From 41 to 50 years	120	3.56	0.70
	>50 years	62	3.50	0.67
	Total	386	3.45	0.80
Preparedness	<30 years	49	3.05	0.91	3.927	0.009	Valid
	From 31 to 40 years	155	3.41	0.80
	From 41 to 50 years	120	3.46	0.82
	>50 years	62	3.54	0.72
	Total	386	3.40	0.82
Response	<30 years	49	3.29	0.81	0.757	0.519	Not Valid
	From 31 to 40 years	155	3.43	0.74
	From 41 to 50 years	120	3.40	0.72
	>50 years	62	3.50	0.66
	Total	386	3.41	0.73
Recovery and Lessons Learned	<30 years	49	3.16	0.81	0.899	0.442	Not Valid
	From 31 to 40 years	155	3.33	0.83
	From 41 to 50 years	120	3.40	0.84
	>50 years	62	3.34	0.86
	Total	386	3.33	0.84
Level of readiness	<30 years	49	3.21	0.77	1.649	0.178	Not Valid
	From 31 to 40 years	155	3.39	0.72
	From 41 to 50 years	120	3.45	0.70
	>50 years	62	3.47	0.62
	Total	386	3.40	0.71

shows that there are statistically significant differences between the preparation and preparedness stage due to age differences. The different ages of the sample have a significant impact on the preparedness phase (p value 0.009). In respect of the remainder of the stages, there were no statistically significant differences between the mitigation, the response and the recovery phases, along with the level of readiness due to the different age of the research sample. Whilst not explicit in the data, the results here may be due to the nature of human beings that unite and interact in difficult times when sensing near danger.

In relation to educational qualifications, it is evident there are statistically significant differences between the mitigation, response and recovery due to different educational qualifications of the research sample as indicated in

Table 4. Results of the F-test between levels of readiness and educational qualification.

Phase	Educational Qualification	No.	Weighted Average	Standard Deviation	F Value	Significance Level	Statistical Significance
Mitigation	Secondary and lower	47	3.47	0.78	2.965	0.032	Valid
	Diploma	67	3.65	0.87
	Bachelor	203	3.35	0.78
	Postgraduate	69	3.56	0.75
	Total	386	3.45	0.80
Preparedness	Secondary and lower	47	3.40	0.72	2.973	0.032	Valid
	Diploma	67	3.65	0.86
	Bachelor	203	3.31	0.80
	Postgraduate	69	3.44	0.87
	Total	386	3.40	0.82
Response	Secondary and lower	47	3.36	0.80	2.712	0.045	Valid
	Diploma	67	3.63	0.77
	Bachelor	203	3.35	0.68
	Postgraduate	69	3.42	0.75
	Total	386	3.41	0.73
Recovery and Lessons Learned	Secondary and lower	47	3.27	0.84	1.801	0.147	Not Valid
	Diploma	67	3.53	0.88
	Bachelor	203	3.27	0.80
	Postgraduate	69	3.37	0.87
	Total	386	3.33	0.84
Level of readiness	Secondary and lower	47	3.38	0.67	3.168	0.024	Valid
	Diploma	67	3.62	0.76
	Bachelor	203	3.32	0.68
	Postgraduate	69	3.45	0.72
	Total	386	3.40	0.71

. The difference in the educational qualification of the research sample has a significant effect on the early stages of disaster management. There is no statistically significant difference between the recovery phase (p value is 0.147) due to the difference in the academic qualification.

In respect of the job title, the analysed data suggests there are no statistically significant (insignificant p values) differences between the different stages (mitigation, preparedness, response and recovery) and the level of readiness due to the different job titles (

Table 5. Results of the F-test between levels of readiness and job title.

Phase	Job Title	No.	Weighted Average	Standard Deviation	F Value	Significance Level	Statistical Significance
Mitigation	Secretary	2	3.79	0.30	1.257	0.276	Not Valid
	Director General	12	3.89	0.88
	Department Chair	31	3.52	0.71
	Undersecretary of Ministry / Emirate	2	3.93	0.51
	Mayor	20	3.69	0.63
	Director of the Department	68	3.43	0.73
	Employee	251	3.41	0.83
	Total	386	3.45	0.80
Response	Secretary	2	3.50	0.00	1.171	0.321	Not Valid
	Director General	12	3.97	0.87
	Department Chair	31	3.39	0.80
	Undersecretary of Ministry / Emirate	2	3.60	0.85
	Mayor	20	3.43	0.80
	Director of the Department	68	3.46	0.70
	Employee	251	3.36	0.85
	Total	386	3.40	0.82
Recovery	Secretary	2	3.39	0.08	1.059	0.387	Not Valid
	Director General	12	3.87	0.78
	Department Chair	31	3.41	0.75
	Undersecretary of Ministry / Emirate	2	3.22	0.79
	Mayor	20	3.52	0.57
	Director of the Department	68	3.46	0.57
	Employee	251	3.37	0.77
	Total	386	3.41	0.73
Recovery and Lessons Learned	Secretary	2	3.56	0.16	1.728	0.113	Not Valid
	Director General	12	3.99	0.92
	Department Chair	31	3.20	0.81
	Undersecretary of Ministry / Emirate	2	3.67	0.31
	Mayor	20	3.49	0.74
	Director of the Department	68	3.37	0.76
	Employee	251	3.29	0.86
	Total	386	3.33	0.84
Level of readiness	Secretary	2	3.56	0.06	1.485	0.182	Not Valid
	Director General	12	3.93	0.82
	Department Chair	31	3.38	0.65
	Undersecretary of Ministry / Emirate	2	3.60	0.61
	Mayor	20	3.53	0.57
	Director of the Department	68	3.43	0.61
	Employee	251	3.36	0.74
	Total	386	3.40	0.71

).

The data analysed in respect of academic specialisation suggests, as indicated in

Table 6. Results of the F-test between levels of readiness and academic specialisation.

Phase	Academic Specialisation	No.	Weighted Average	Standard Deviation	F Value	Significance Level	Statistical Significance
Mitigation	Literary	96	3.38	0.75	0.7620	0.5510	Not Valid
	Scientific	233	3.47	0.82
	Commercial	21	3.49	0.81
	Industrial	35	3.48	0.79
	Other	1	4.57	.
	Total	386	3.45	0.80
Preparedness	Literary	96	3.34	0.70	1.072	0.3700	Not Valid
	Scientific	233	3.41	0.86
	Commercial	21	3.50	0.87
	Industrial	35	3.45	0.85
	Other	1	4.90	.
	Total	386	3.40	0.82
Response	Literary	96	3.36	0.69	0.586	0.673	Not Valid
	Scientific	233	3.42	0.74
	Commercial	21	3.51	0.79
	Industrial	35	3.46	0.75
	Other	1	4.22	.
	Total	386	3.41	0.73
Recovery and Lessons Learned	Literary	96	3.27	0.85	1.124	0.345	Not Valid
	Scientific	233	3.33	0.83
	Commercial	21	3.47	0.85
	Industrial	35	3.42	0.83
	Other	1	4.78	.
	Total	386	3.33	0.84
Level of readiness	Literary	96	3.34	0.65	1.088	0.362	Not Valid
	Scientific	233	3.41	0.72
	Commercial	21	3.49	0.74
	Industrial	35	3.45	0.75
	Other	1	4.62	.
	Total	386	3.40	0.71

, there are no statistically significant (insignificant p values) differences between the different stages (mitigation, preparedness, response and recovery) and the level of readiness due to different academic specialisation of the research sample. The difference in the academic level of the research sample has no significant effect on the stages of disaster management. This may be due to the advantages that the employee has created in light of the scarcity of specialisations required in this field.

In respect of the analysis of the data related to the number of courses a survey respondent has completed in the field of disaster management, there are significant differences between the various stages of mitigation (p value 0.001), preparedness (p value 0.005), response (p value 0.007) and recovery (p value 0.015) as well as the level of readiness (p value 0.001) due to the number of short courses in disaster management completed by respondents. The difference in the number of short courses completed by the research sample has a significant effect on all stages of disaster management.

The second hypothesis in this study states that “there is no statistically significant effect on the human, organisational, technological, coordination, environmental factors individually and collectively in the readiness levels of government departments concerned in the four stages of disaster management”. In this section, the present study has used the multiple regression approach based on the Stepwise method to enter and test the variables in the model. The regression model only contains the independent variables listed with the partial correlation with the dependent variable in the existence of independent variables arranged by link strength.

In

Table 7. Hypothesis 2 and multiple regression model.

Phases of Disaster Management	Equation	p-Value	R Square Value
Mitigation	Y1 = 2.64 + 0.265 X2 + 0.181 X3 + 0.402 X4 + 0.275 X5	0.000	0.240
Preparedness	Y2 = 2.713 + 0.226 X2 + 0.289 X3 + 0.404 X4 + 0.304 X5	0.000	0.273
Response	Y3 = 2.747 + 0.214 X2 + 0.246 X3 + 0.364 X4 + 0.285 X5	0.000	0.277
Recovery	Y4 = 2.484 + 0.110 X1 + 0.353 X2 + 0.218 X3 + 0.304 X4 + 0.160 X5	0.000	0.341
Level of readiness	Y = 2.673 + 0.257 X2 + 0.260 X3 + 0.427 X4 + 0.228 X5	0.000	0.353

, Hypothesis 2, it is seen that there is a statistically significant (p value 0.000) impact of the human, organisational, technological, coordination and environmental factors individually and collectively in the readiness levels of the government departments concerned with the four phases: mitigation, preparedness, response and recovery. Significant linear models were obtained for the impact of factors human, organisational, technological, coordination, and environmental in the level of readiness of government departments in all the disaster management phases. In review of the data associated with Hypothesis 2, it is important to recognise the data emanated from a 1–5 point scale.

The trends of the research sample were as follows:

• Humanitarian factors: The data analysis revealed the effect of this factor as a whole is neutral and is consistent with the weighted average of the responses at 2.88 (neutral), with the accumulative influence having potential impact on productivity.
• Organisational factors: The data analysis identified that the effect of this factor as a whole is neutral and consistent with the weighted average of responses at 3.18 (neutral). However, in analysing the answers, there are indications of an imbalance in the approach undertaken to deal with crisis. In this regard, the data analysis suggests the potential for crisis management to be dealt with by leadership selected for the circumstances, based on training and qualification and leadership qualities irrespective of rank.
• Technological factor: In this study, the data analysis revealed the effect of this factor as a whole is neutral and consistent with the weighted average of responses at 3.24 (neutral). The indications here suggest for organisations to be supported by technological capabilities that are adequate and appropriate to deal with crises and enable high quality and efficient communication systems to provide decision makers with accurate and up-to-date information capable of producing necessary analysis of crisis situations.
• Environmental factors: The data analysis here suggests the effect of this factor as a whole is consistent with the weighted average of responses at 3.44. There is potential here for urban planners to consider the implication of these results for examination of construction systems vs population density, aligned to level of expected risk.
• Coordination factors: The effect of this factor as a whole in this study is neutral and is consistent with the weighted average of responses at 3.13 (neutral). However, in analysing the answers, the trend suggests each of these stages has tasks and responsibilities that require maximum coordination and cooperation to achieve the expected outcome at each stage. A potential consideration drawn from the data is to create a database and activate participation of civil society institutions by informing on the size and potential area/s in which it can contribute for the benefit of the whole of community and organisational approach to DRR.

6. Discussion

In the current climate of post-COVID-19 for many countries, it is opportune to enable a contribution to the literature associated with identifying influences on the preparedness for each phase of DRM as perceived by those responsible for DRM. Whilst the current study centred on the case for KSA, the findings are to the DRM community, particularly as the study explored the influence of specific demographic factors of DRM employees.

The data suggests there are significantly different influences on the preparation and preparedness stage due to age differences. The influence of age was also noted by Brofman [19] as influencing the preparedness phase. Bronfman [19] suggested it is the older age group with the highest level of preparedness and the KSA study would also support this conclusion.

Nikkanen [18] suggests education level has a marginal influence on disaster response preparedness. The KSA study suggests there is a difference in the first three stages of disaster response influenced by education status/qualification with no significant difference at the recover stage. In terms of personal resilience, as suggested by Drzewiecki [16] the higher level of education the more resilient the person. Interestingly, in respect of the number of short courses in DRM completed by KSA employees, the trend from the data analysis suggests this influences all four stages of disaster management. An important implication here is that the positive role of providing DRM-related education to those working in the disaster response field has the potential to enhance the organisational level of preparedness. This is not unexpected and the KSA study offers validated support for investment by organisations in education programs for their disaster response employees. Of note, the KSA study findings suggest there is no statistically significant difference in the academic specialisation influence on the four stages of disaster management. It would require further nuanced research to understand more specifically the reason for this trend, however, it is not unreasonable to suggest those members with academic specialisation would work within the phase of DRM that matches their specialisation.

Analysis of the influence of the position held (job title) of the KSA study sample of employees revealed no statistically different influence on the four states of DRM. A reasonable explanation for this outcome is the purposeful sampling of the survey respondents, all of whom work within departments responsible for DRM and all are members of civil defence committees (with responsibility for DRM). Position title/role has not previously been identified in published research and potentially is of value to KSA and continuous improvement initiatives at this time, whilst being of potential consideration for the wider DRM governance decision makers. Consideration of this element is helpful in the context of education initiatives for DRM employees in respect of the level and format of education qualification to be aligned to the position/role within the DRM staffing. In addition, one of the factors discussed in the literature is the level of previous experience with disasters and emergencies [11,17] enhancing the level of preparedness for DRM and whilst this was not specifically a dimension within the KSA study, it is not unreasonable to suggest the positions held by the survey sample (members of civil defence committees) would indicate a level of experience. This is particularly relevant in the context of the information in Table 1 presenting the disasters and emergencies that have occurred in the Kingdom.

As suggested earlier, the results in respect of the influence of the region from which the survey respondent is located has a significant influence in each of the four stages of disaster risk management. The diversity of the geography, population, weather and potential for natural hazards are elements within the environment/location and key elements combining to influence the level of preparedness at each phase of disaster management within KSA. This resonates with a wealth of literature exploring the environmental location influences on DRM as indicated in the work of Alam [5], Alam and Ray-Bennett [6] and AlQahtany and Abubakar [24].

The data analysis in respect of the 2nd hypothesis suggests there is an impact on the level of preparedness for disasters and emergencies by human, organisational, technological and environmental factors individually and in combination. Whilst the overall trend is not new knowledge, as suggested in the literature these elements do have an impact, but the findings that are not readily available in the literature is the relativity of these factors. The KSA study indicates there is no significant difference in the level of influence of these factors on the level of organisational preparedness. The conclusions in the work of Wachinger [21] align with the KSA study findings, i.e., there is influence at all stages of DRM by the human factor. This is not an unexpected conclusion on the basis that it is human intervention that is responsible for management of the four phases of disaster response. It is interesting that in the KSA study there was limited differentiation between the human, technological, environmental and organisational factors individually or collectively on the level of overall organisational preparedness.

Table 8. Comparative findings (KSA Study / Previous Studies).

Literature	Literature Finings	KSA Study
[16] Drzewiecki, D.M., Wavering, H.M., Milbrath, G.R., Freeman, V.L. & Lin, J.Y. The association between educational attainment and resilience to natural hazard-induced disasters in the West Indies: St. Kitts & Nevis.	Adults with higher education more resilient	Extends the details of education including significant influence of DRM short course completion.
[18] Nikkanen, M., Räsänen, A. & Juhola, S. The influence of socioeconomic factors on storm preparedness and experienced impacts in Finland	Education and employment status not connected to respondents’ preparedness levels. Socio-demographic factors have marginal influence on storm preparedness or experienced impacts in Finland, which contradicts earlier research	KSA study extends the knowledge related to education and influence on 4 phases of DRM—concluding the level of education has more impact in the earlier phases of DRM
[32] Orimoloye, I.R., Belle, J.A. & Ololade, O.O. Exploring the emerging evolution trends of disaster risk reduction research: a global scenario	DRM research systematic review 1990–2019 This study concludes that the DRR-related research hotspots are focused primarily on disaster management and science, environmental science, climate change and ecosystem services.	KSA study confirms disaster management is a research hotspot, the KSA study adding to the body of literature
[19] Bronfman N.C., Cisternas, P.C., Repetto, P.B. & Castaneda, J.V. Natural disaster preparedness in multi-hazard environment: Characterizing the sociodemographic profile of those better (worse) prepared.	The sociodemographic profile of individuals with the highest levels of preparedness in an environment with multiple natural hazards are people between 30 and 59 years of age	Table 3 shows that there are statistically significant differences between the preparation and preparedness stage due to age differences. The different ages of the sample have a significant impact on the preparedness phase (p value 0.009). In respect of the remainder of the stages, there were no statistically significant differences between the mitigation, the response and the recovery phases, along with the level of readiness due to the different age of the research sample
[10] Andersson, T., Caker, M., Tengblad, S., Wickelgren, M. Building traits for organisational resilience through balancing organisational structures.	Risk awareness is the basic trait	KSA study extends the demographic requirements for organisational resilience for DRM
[13] Saja, A.M.A., Teo, M., Goonetilleke, A. & Ziyath, A.M. An inclusive and adaptive framework for measuring social resilience to disasters	There is no consensus on how to measure social resilience, though a wide range of methods have been proposed. Some social resilience frameworks have been developed specific to a particular hazard and some other frameworks for a specific geographical area.	The KSA study offers a potential approach to measuring social resilience through adapting the survey for measuring community members perspective on disaster preparedness.
[8] Titko, M. and Ristvej, J., Assessing Importance of Disaster Preparedness Factors for Sustainable Disaster Risk Management: The Case of the Slovak Republic	According to the results, younger people incline more to realising the protection measures. This finding can be partially surprising, but the cause can be found in a more active approach of the young people, the ability to solve problems, and their higher awareness of threats. Closer research of the relation age–preparedness confirms this fact. The correlation dependence of the age and objective preparedness increased to r = −0.34, p < 0.01 and the dependence of the age and the subjective preparedness increased to r = −0.16, p < 0.01. The younger respondents also assess their preparedness on average higher that the older ones. However, the regression analysis did not confirm any significant relation of the age and subjective preparedness.	The KSA study with DRM employees suggests there are statistically significant differences between the preparation and preparedness stage due to age differences. The different ages of the sample have a significant impact on the preparedness phase (p value 0.009). In respect of the remainder of the stages, there were no statistically significant differences between the mitigation, the response and the recovery phases, along with the level of readiness due to the different age of the research sample.
[31] Bracci, E., Tallaki, M., Gobbo, G. & Papi, L. Risk management in the public sector: a structured literature review.	The authors call for an increase in research associated with DRM and the public sector	The KSA study is situated within the public sector and the findings contribute to close gaps in understanding the influential elements within the public sector that impact successful DRM
[9] Oh, N. & Lee, J. Changing landscape of emergency management research: A systematic review with bibliometric analysis.	Communication, collaboration, citizen participation, risk perception and vulnerability	The KSA study offers additional dimensions in understanding the influential elements on levels of disaster and emergency preparedness

is presented to illustrate the relationship between the research discussed in the literature and respective findings aligned to the current KSA study findings.

7. Conclusions and Recommendations

To place the value of this study into context, it is helpful to consider the findings in three areas of potential contribution, i.e., theoretical implications, practical implications, limitations of the study and future work. The following Section 7.1, Section 7.2 and Section 7.3 are discussed in the context of adaptability and transfer to the global community in which agencies are continuously defining and redefining approaches to disaster risk reduction.

7.1. Theoretical Implications

A theoretical implication is established when a theory or hypothesis is tested, and the results support the theory. In the current KSA study, the results do not support Hypothesis 1: there are no statistically significant differences in the readiness levels of the relevant government departments due to the demographic characteristics of age, academic qualification, job title, academic specialisation, number of courses in disaster management and the region to which the organisation belongs. In respect of the KSA study Hypothesis 2: there is no statistically significant effect on the human, organisational, technological, coordination or environmental factors individually and collectively in the readiness levels of government departments concerned in the four stages of disaster management. The data analysis does not support this hypothesis.

7.2. Practical Implications

There is a range of practical implications emanating from the KSA study. Firstly, in response to the trend indicating a difference in the level of readiness due to the education standard and the number of DRM short courses completed by personnel highlights the need for organisations to consider building capacity and capability through relevant education initiatives. The collective indications are for an emphasis on the critical importance of the last phase of crisis management—the learning phase—the key to gaining a competitive advantage in today’s work environment is to focus on activating the concept of a “learning organisation”. A learning organisation reflects the type of organisation that is constantly learning or taking advantage of their experiences. It is related to the concept of “educated organisation” that it is necessary to emphasize the importance of knowledgeable employees and the ability to share that knowledge with their colleagues and associates. The role played by the human element in each stage of crisis management calls for the need to pay attention to the formation of adequate intellectual capital to meet the continuing and growing challenges facing organisations, authorities and nations in the field of DRR. The second practical implication relates to the difference identified in the level of preparedness perceived dependent on the region in which the department and personnel are located. Here also there is an interdependence on education initiatives to enable those who have limited experience in working within a DRM response to be rotated, attached or volunteered to support areas experiencing a crisis in order to build their knowledge and capacity.

7.3. Limitations and Future Work

Developing future studies with a focus on exploring the nuanced rationale for the results where there are lower levels of preparedness and/or demographic implications limiting the level of preparedness is an important area. Appreciatively, this is a limitation within the current study. However, the KSA study offers baseline data from which to develop targeted research studies to more adequately build the body of knowledge specifically in the area of the role of education and experience in influencing levels of organisational preparedness. This approach provides a conduit for building knowledge applicable beyond the Kingdom of Saudi Arabia. Such future studies would be enhanced through combining qualitative and quantitative data, interviews and questionnaires with opportunity for respondents to explain the meaning behind their responses by adding a richness to understanding the minutiae underpinning quantitative survey data.

The study presented here offers a contribution to the body of knowledge for DRR through insight into the demographic factors to be considered when developing systems and organisational resilience in preparedness and management of crisis and disasters. As suggested by Alam and Ray-Bennett, 2021; Mondal et al., 2018; Cvetković et al., 2021 and Shah et al., 2019, the human factor is vital in the field of DRR and through developing and demonstrating effective organisational capability, community trust will be enhanced. Developing such organisational capability is informed by understanding the requisite demographic attributes of personnel required to establish and maintain efficient and effective DRR preparedness and response mechanisms for the future safety across local, national and international disaster and crisis response agencies.