Factors Affecting Flood Disaster Preparedness and Mitigation in Flood-Prone Areas in the Philippines: An Integration of Protection Motivation Theory and Theory of Planned Behavior

Abstract

Natural hazards are one of the destructive phenomena that pose a significant hazard to humans, property, and the economy, among others. One of the most recurring natural hazards is flooding, which is caused by typhoons, monsoons, and heavy rainfall and has been one of the main concerns of the Philippines in recent years. The study’s results will provide information on the factors affecting flood disaster preparedness by integrating the Theory of Planned Behavior (TPB) and the Protection Motivation Theory (PMT). A total of 509 individuals answered an online survey questionnaire with 52 adapted questions. Structural equation modeling (SEM) revealed that risk perception (RP), media (M), and personal experience (PE) had an effect on perceived severity (PS) and perceived vulnerability (PV), which consequently affected the attitude toward the behavior (ATB), social norms (SN), and perceived behavioral control (PBC). It was determined that ATB, SN, and PBC significantly affected adapted behavior (AB), which consequently led to intention to follow (ITF) and perceived prevention (PP). After analyzing the data, it was revealed that 56.2% of female respondents were said to be more disaster resilient compared to males. This is the first study to determine the perceived prevention of disaster preparedness and mitigation in flood-prone areas in the Philippines. The results will be beneficial to academicians and government officials in developing determining factors that affect flood disaster preparedness. Lastly, a deeper understanding of how AB is the most significant variable may be further researched to improve the paper.

1. Introduction

Natural hazards are one of the destructive phenomena that pose a significant hazard to humans, property, and the economy, among others. [1,2]. Natural hazards dramatically affect the economy and humans; it was recorded that there were 2,599,237 fatalities and 6,354,195 injuries due to natural hazards from 1980 to 2015 [3]. The Philippines, being located in the Pacific Typhoon Belt and an archipelagic country, is highly exposed to various natural hazards [4]. Typhoons, floods, droughts, and earthquakes are some of the most recurring disasters that the country has encountered over the past decades. Occurring about 20 times per year, typhoons are considered the most frequent natural hazard in the Philippines, and floods are highly likely to happen in countries with recurring typhoons [5]. After the recent Typhoon Vamco (locally known as Typhoon Ulysses) were there reported to be 39 casualties and 22 people missing [6]. According to the Emergency Event Database of the Philippines (EMDAT) [7], there were a total of 2847 people killed from 1978 to 2018. Diseases like leptospirosis, cholera, and hepatitis are some of the conditions one can potentially acquire from floods [8]. From 1978 to 2018, the total economic damage due to floods was PhP 180 billion in the Philippines. A considerable portion of this damage affected the agricultural sector, which totaled PhP 5.2 million [9].

Having established that disasters often occur, it is clear that there is a need to manage these destructive phenomena to mitigate damages and prepare for, respond to, and recover from them. For that reason, the Disaster Management Cycle was created, having mitigation, preparedness, response, and recovery as its four phases [2]. The said cycle was formulated with the goals of (a) avoiding and decreasing damages from disaster, (b) guaranteeing immediate assistance to victims, and (c) having a swift and successful recovery [10]. For this cycle to be implemented effectively, the full participation of everyone, most especially organizations, local government units (LGUs), and the national government, is required. According to Warfield [11], existing policies and plans must be regulated to adapt to the causes of disasters and reduce their effect on people, property, and infrastructure. Disaster Preparedness and Mitigation are the two before-event phases of the Disaster Management Cycle. Phase 1 of said cycle is Mitigation. This phase refers to the action that would reduce the severity and effect of the disaster [12]. Phase 2, or Preparedness, refers to knowing the possible impact of the disaster and taking action in case of any emergency [10]. Since the two said stages are critical in determining the implications of a disaster [13], it is vital that individuals are well-informed about preparing and mitigating the effects of disastrous events. These two phases are critical in understanding how a country responds to disasters, specifically floods [14]. According to studies, catastrophic disasters can be avoided with effective preparedness and mitigation activities [15]. Researchers have proven that it is crucial to have mandated mitigation and preparedness activities in order to avoid further damages brought by catastrophic disasters [14,15].

The findings of this study will provide information on the factors affecting disaster preparedness that could become a baseline for the practices to be done to reduce damages during said events. Determining the factors involved is beneficial to understanding how people perceive disasters and where national and local government units could derive responses to these calamities when they occur. This applies to policymaking, where the results of the assessment to be conducted will be integrated into policies on disaster risk management with the goal of having optimal and sustainable responses, such as maximizing resources and preventing losses or casualties. In addition, the academe could enhance the existing preparedness guidelines according to the results of the study and set a priority on the significant areas of these factors that could further help improve the development of the evacuation and safety precaution processes.

This research had the goal of determining the statistically significant factors affecting flood disaster prevention and mitigation in flood-prone areas, integrating the following theories: Protection Motivation Theory (PMT) and Theory of Planned Behavior (TPB), through the Structural Equation Modeling (SEM) approach. This study also aimed to recommend the application of the findings gathered as a basis for improving the current flood disaster preparedness and mitigation in the Philippines, to avoid or lessen the detrimental effects of floods.

Specifically, this study aimed to: (1) identify the current state of flood disaster preparedness and mitigation in countries that utilize the Disaster Management Cycle and compare it with the current preparedness and mitigation plan of the Philippines; (2) discover the current challenges faced by local government units and NDRRMC in implementing a standardized flood disaster preparedness and mitigation plan and use this information in developing the study; (3) assess the perception and behavioral aspects of Filipinos residing in flood-prone areas in the Philippines with regards to flood disaster preparedness and mitigation through a survey questionnaire authored by the proponents; (4) determine the influence of the factors stated in the study on flood disaster prevention and mitigation; and (5) compose research contributing to the Philippines’ studies on flood disaster preparedness and mitigation, which can be utilized to mandate future flood disaster preparedness plans.

2. Conceptual Framework

Figure 1 presents the conceptual research framework of this study. This research utilizes the integration of Protection Motivation Theory (PMT) and the Theory of Planned Behavior. This research framework presents the aforementioned research variables, which were gathered from related pieces of literature. The research variables shown in this framework are the various factors that affect flood disaster prevention and mitigation in flood-prone areas.

Hashim et al. [16] defined risk perception as a perception of the degree of risk or damage from the result of a hazard or disaster. Risk perception refers to the subjective assessment of a specific accident that is currently happening and how one is concerned with its consequences [17]. People have negative feelings when they perceive that a flood event is of high risk based on their assessment of its severity [18]. People that are usually close to hazard-prone areas see themselves as more vulnerable to threats caused by natural hazards like floods [4]. It was also discussed by Hashem et al. [16] that risk perception is the most consistent factor influencing preparedness actions [19]. Thus, the following were hypothesized:

Hypothesis 1 (H1).

Risk perception has a significant effect on a person’s perceived severity.

Hypothesis 2 (H2).

Risk perception has a significant effect on a person’s perceived vulnerability.

In the era of the internet, social media is growing rapidly and is considered an important source of additional information that can be used in the analysis of crisis and disaster response [20]. According to Arapostathis [21], a number of studies have explored utilizing social media for effective Disaster Management (DM) over the last ten years. Communication is significant for coordination in flood management. It was discussed by Kertawidana [22] that social media is an important tool of communication that can aid the coordination of their agencies in flood management in the city of Semarang. Social media could greatly help improve risk awareness and aid in reducing the people’s sense of vulnerability by the use of mitigation and preparedness plans [18]. Thus, the following were hypothesized:

Hypothesis 3 (H3).

Media has a significant effect on a person’s perceived severity.

Hypothesis 4 (H4).

Media has a significant effect on a person’s perceived vulnerability.

It is reported that having previous experiences with disasters would greatly influence the engagement in disaster preparedness action [16]. Kurata et al. [4] was also able to state that personal experiences, alertness, and preparedness would be contributing indicators of one’s perceived severity. Kurata et al. [23] also stated that people who have past experiences with hazardous events or disasters have more anxiety and are more likely to join community preparedness activities than those with no past experiences. According to Kurata et al. [23], past experiences of disastrous events have effects on people’s upcoming perception and evaluation of risk and their decision making. Thus, the following were hypothesized:

Hypothesis 5 (H5).

Personal experience has a significant effect on a person’s perceived severity.

Hypothesis 6 (H6).

Personal experience has a significant effect on a person’s perceived vulnerability.

People’s perception of flood severity or consequences increases hazard-related anxiety due to their belief of a higher probability of future floods [23]. According to Altarawneh et al. [18], people who perceived a flood as high risk based on their perceived severity would feel more anxious, powerless, angry, and afraid. Anxiety plays a role in influencing preparedness and thinking; it was observed by Kurata et al. [23] that there was a negative effect on the perceived severity of anxiety, although it was originally proposed as a positive result. Even though, negatively, anxiety is a driving factor for a prepared attitude, Huang et al. [24] also states that a flood risk attitude influences one’s coping behavior. In becoming more prepared for future flood events, aside from self-efficacy, social norms are also important in aligning people’s collaboration and the maintenance of social order in times of crisis. According to Kurata et al. [25], indicators like personal experience, levels of alertness, and preparedness would play a part in a person’s belief in the possible occurrence of flood disasters and were determined as important indicators of perceived severity. Thus, the following were hypothesized:

Hypothesis 7 (H7).

Perceived severity has a significant effect on a person’s attitude toward the behavior.

Hypothesis 8 (H8).

Perceived severity has a significant effect on a person’s social norms.

Hypothesis 9 (H9).

Perceived severity has a significant effect on a person’s perceived behavioral control.

People that reside in close proximity to hazard-prone areas would add to their own vulnerability to threats caused by natural hazards in their area [25]. It is important to properly assess one’s vulnerability. If the perceived vulnerability due to risk of floods is not acknowledged, the government will not be able to earnestly understand the vulnerability of its residents in flood disasters [26]. Involving pre-disaster preparation, especially essential supplies, it is shown that one’s behavior intentions are caused by risk perception [27]. It is necessary to have risk awareness among the population, for it would greatly help in reducing the perceived vulnerability by improving the quality of mitigation and preparedness plans [18]. Thus, the following were hypothesized:

Hypothesis 10 (H10).

Perceived vulnerability has a significant effect on a person’s attitude toward the behavior.

Hypothesis 11 (H11).

Perceived vulnerability has a significant effect on a person’s social norms.

Hypothesis 12 (H12).

Perceived vulnerability has a significant effect on a person’s perceived behavioral control.

Attitude toward behavior refers to an individual’s positive or negative assessment of his or her own performance of a certain behavior. This concept relates to the degree to which the execution of an activity is regarded positively or negatively. It was discussed by Valois et al. [28] that people with more negative effects on themselves adopt more adaptive behaviors during flood events. It is established by a vast list of behavioral principles that tie behavior to a wide range of outcomes and other qualities [29]. In a study by Huang et al. [24] in Shenzhen, China, the sole direct factor that influences individual coping behavior is flood risk attitude. This reflects a person’s judgment on their ability to prevent threats in relation to PMT. A study by Sumaedi et al. [30] conducted research on the variables influencing people’s decision to stay at home during the COVID-19 epidemic; this research revealed that one’s attitude toward the behavior had a favorable and significant impact on following the policy. Thus, it was hypothesized that:

Hypothesis 13 (H13).

Attitude toward the behavior has a significant effect on a person’s adapted behavior.

An overview of reviews regarding social norms literature by Legros and Cislaghi [31] discovered that there are three points of agreement on this concept. Social rules are firstly necessary to be socially acceptable in some way and have a role in action-oriented decision-making and an impact on an individual’s health and well-being. According to Nguyen et al. [32], the impact of the COVID-19 pandemic, which includes subjective norms such as wearing face masks and staying in, may lead people to adhere to preventive measures such as living at home, limiting public events, maintaining personal and social separation, and basic grooming because they were influenced by their community. It is then discussed in the study by Geber and Hefner [33] that the relationship between norms and behavior is modeled with the expectation of communication with referent persons and the expectation of group members observing one’s behavior. Thus, it was hypothesized that:

Hypothesis 14 (H14).

Social norms have a significant effect on a person’s adapted behavior.

One of the factors that affected behavior during flood disasters is how older adults are literate on flood preparedness [34]. The application of a TPB study by Soorani and Ahmadvand [35] stated that the most significant behavioral predictor is perceived behavioral control. It was concluded that in order to view a person’s adapted behavior, it is significant to examine his or her perception on having difficulties of enacting the said behavior. In the view of climate change in general, adaptation behavior showed that risk perceptions have a direct impact on mitigation behavior intentions. This involves pre-disaster preparation, such as the preparation of food, water, and evacuation supplies [27]. Thus, it was hypothesized that:

Hypothesis 15 (H5).

Perceived behavioral control has a significant effect on a person’s adapted behavior.

The term adapted behavior refers to measures aimed at preventing or limiting the expected or negative impacts of flood episodes. Such actions can be conducted before, during, or after an occurrence [36]. According to the findings, those who have greater negative impacts on their physical or mental health acquire more adaptable behaviors during a flood that does not need evacuation, as well as post-flood adoption behaviors, than those who have few or no bad consequences on their health [28]. In most relevant scenarios, such as commuting in the Philippines, commuters rely on government agencies, specifically public transportation preparation, in response to floods [37]. Thus, it was hypothesized that:

Hypothesis 16 (H16).

Adapted behavior has a significant effect on intention to follow.

In disaster preparedness, not all areas in the Philippines cover the necessary service needed in response to disaster events. A study by Dariagan et al. [38] indicated that most villages are unable to operate their rescue and relief teams due to a lack of financial resources. This willingness is demonstrated by their amount of effort to perform in relation to TPB [30]. People are also more likely to follow government and community directives when they are confident in their ability to avoid infection and have sufficient awareness about COVID-19 [32]. In flood risk perception study, preparedness is compared to worry and awareness as separate issues. There has been a thorough investigation of the elements that influence taking preventive measures concerning flood risk perception [39]. The findings of the study by Kurata et al. [4] emphasized that as people become more informed, educated, and active in adapting to disaster prevention measures established by the local government units, the probability of casualties and harmful flood impacts will be minimized. Thus, it was hypothesized that:

Hypothesis 17 (H17).

Intention to follow has a significant effect on perceived prevention.

3. Methodology

3.1. Participants

A total of five hundred nine (509) individuals residing in the Philippines filled the survey questionnaire willingly (

Table 1. Respondents’ descriptive statistics (N = 509).

Characteristics	Category	N	%
Gender	Female	286	56.2
	Male	210	41.3
	Prefer not to say	13	2.6
Age	15–24 years old	408	80.1
	25–34 years old	22	4.4
	35–44 years old	18	3.6
	45–54 years old	27	5.4
	55–64 years old	33	6.6
	Above 65 years old	1	0.2
Personal Monthly Income	Less than PhP 10,957	327	64.2
	Between PhP 10,958 to PhP 21,914	61	12.0
	Between PhP 21,915 to PhP 43,828	40	7.9
	Between PhP 43,829 to PhP 76,699	29	5.7
	Between PhP 76,700 to PhP 131,484	28	5.5
	Between PhP 131,485 to PhP 219,140	14	2.8
	At least PhP 219,141	10	2.0
Marital Status	Single	438	86.1
	Married	57	11.2
	Widowed	6	1.2
	Annulled/divorced	2	0.4
	Separated	5	1.0
	Registered partnership	1	0.2
Region	Region 1—Ilocos Region	7	1.4
	Region 2—Cagayan Valley	3	0.4
	Region 3—Central Luzon	125	24.8
	Region 4A—CALABARZON	113	22.3
	Region 4B—Southwestern Tagalog	4	0.8
	Region 5—Bicol Region	2	0.4
	Region 7—Central Visayas	2	0.4
	Region 8—Eastern Visayas	2	0.4
	Region 10—Northern Mindanao	1	0.2
	Region 11—Davao Region	4	0.8
	Region 12—SOCCSKSARGEN	3	0.2
	NCR—National Capital Region	243	48.1
Type of Residential House	2-storey house	6	1.2
	3-storey house	4	0.8
	4-storey house	1	0.2
	Apartment	19	3.7
	Bungalow	105	20.6
	Compound	5	1.0
	Condominium	38	7.5
	Informal settlement	9	1.8
	Single attached	124	24.4
	Single detached	94	18.5
	Townhouse	104	20.4
Number of Household Members	1 or living alone	2	0.4
	2 persons	26	5.1
	3 persons	71	13.9
	4 persons	129	25.3
	5 persons	115	22.6
	6 persons	79	15.5
	7 persons	34	6.7
	8 persons	25	4.9
	9 persons	11	2.2
	10 persons	7	1.4
	11 persons	2	0.4
	12 persons	3	0.6
	14 persons	2	0.4
	15 persons	2	0.4
	19 persons	1	0.2
Educational Attainment	College graduate	92	18.1
	College undergraduate	296	58.2
	High school graduate	95	18.7
	High school undergraduate	10	2.0
	Post baccalaureate	13	2.6
	Elementary undergraduate and below	1	0.2
	Elementary graduate	2	0.4

). Because of the COVID-19 pandemic, convenience sampling was used, and the questionnaire was administered digitally from the start of September 2022 until November 2022. Utilizing the Yamane Taro equation (Equation (1)), the required sample for generalizability is 400. With 62.6 million residents in the Philippines, the current study opted to consider 500 samples to have adequate representation on the objective of the study.

$$n=\frac{N}{1+N{\left(e\right)}^2}$$

(1)

Based on the demographics, there were 56.2% female respondents and 41.3% male respondents. Most of them were 15 to 24 years old (80.1%), followed by 55 to 64 years old (6.6%), and 45 to 54 years old (5.4%). The respondents’ personal monthly income was mostly less than PhP 10,957 (64.2%) or PhP 10,958 to PhP 21,914 (12.0%). Moreover, most of the respondents were single (86.1%), with 11.2% married. A large percentage, 48.1% of the total respondents, were from the National Capital Region, followed by region 3 Central Luzon (24.8), and Region 4A CALABARZON (22.3%) residents. A more detailed presentation of the location per province or city and barangay can be found in Appendix A. The respondents’ type of residential house was mostly single attached (24.4%), bungalow (20.6), and townhouse (20.4%). They were also able to provide the number of household members in their current setting, with the following breakdown: 4 household members (25.3%), 5 household members (22.6%), 6 household members (15.5%), 3 household members (13.9%). The respondents’ educational attainment mostly were college undergraduates (58.2%), followed by high school graduates (18.7%), and college graduates (18.1%).

3.2. Questionnaire

A self-administered questionnaire (SAQ) is a questionnaire designed to be completed by a respondent without the assistance of the researchers for the collection of data [40]. This questionnaire is developed based on the related literature [3,16,17,19,22,23,25,28,29,39,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62] followed by the conceptual research framework containing the hypotheses of the study discussed in the previous chapter. The research identified in

Table 2. Table of constructs and measurement items.

Construct	Items	Measures	References
Risk Perception	RP1	I think I can handle flood disasters by myself.	[19]
	RP2	I am able to assess the possible risks of flood disasters.	[16]
	RP3	I am able to assess the possible results of flood disasters.	[39]
	RP4	I trust the flood evacuation facilities.	[41]
	RP5	I am able to assess the likelihood of flood events	[17]
	RP6	I am willing to protect and take action before a flood disaster occurs	[42]
Media	M1	I am able to filter reliable and relevant information about flood disasters.
	M2	I frequently use social media to communicate on flood disasters.	[22]
	M3	I frequently use social media to track possible flood disaster events that might happen in the future.	[43]
	M4	I follow news media accounts to inform myself about possible flood disaster events that might happen in the future.
Personal Experience	PE1	I experience flood disasters often.
	PE2	I feel anxious whenever there are reported floods around our area.	[23]
	PE3	I have learned from my past experiences with flood disasters.	[44]
	PE4	My experiences with flood disasters have helped me prepare better when there is a possible flood event that may happen.	[45]
	PE5	I am able to influence my future outcomes when there is a flood event based on experience.	[23]
Perceived Severity	PS1	I am alert and prepared for flood disasters due to perceived threats.	[25]
	PS2	I know that floods are an extreme outcome of typhoons.	[46]
	PS3	I believe that flood disasters may affect my way of life.	[47]
	PS4	I think flood disasters are a severe threat to humans.	[48]
Perceived Vulnerability	PV1	I am in close proximity to hazard-prone areas.	[25]
	PV2	My community is vulnerable to flood disasters.	[47,49]
	PV3	I have experienced being vulnerable to flood disasters before.	[47,49]
	PV4	My family may be affected by flood disasters.	[48]
	PV5	My area is near the assigned local evacuation centers.
	PV6	I am near local responders/flood water rescue teams.
Attitude toward the Behavior	ATB1	I am optimistic about my self-performance in flood disasters.	[29]
	ATB2	I do not feel stressed when there are impending floods.	[50]
	ATB3	I know the flood disaster precautions in my community.	[51]
Social Norms	SN1	My loved ones affect my behavior to prepare for flood disasters.	[52]
	SN2	I feel compelled to prepare and inform myself of flood disasters.	[53]
	SN3	I am inspired to apply flood mitigation measures due to neighbors who do the same.	[54]
	SN4	When it comes to preparing for a natural hazard, I want to do what my friends and family think I should do.	[55]
Perceived Behavioral Control	PBC1	Preventing the possible effects of flood disasters depends on myself.	[56]
	PBC2	I am confident that I can take precautionary measures against the damages of flood disasters.	[57]
	PBC3	I am confident that I have enough knowledge about flood disasters.	[58]
	PBC4	I think preventive protocols are easy to implement	[3]
Adapted Behavior	AB1	I do not experience adverse physical or mental effects due to floods.
	AB2	I always prepare emergency supplies and items whenever there is a possibility of floods.	[56]
	AB3	I connect and build relationships with neighbors when preparing for a flood.	[59]
	AB4	I make changes to the house to make it flood-proof or flood-resistant (e.g., waterproof foundations, replace water-sensitive flooring).	[28]
Intention to Follow	ITF1	I have participated in community preparedness activities and workshops.	[23]
	ITF2	I rely on government agencies, especially public transportation, in flood events.
	ITF3	I intend to follow government officials if they ask me to evacuate even before a flood disaster hits my area.	[49]
	ITF4	I practice evacuation plans on a regular basis.	[60]
	ITF5	I monitor the local government’s announcements during possible flood events.
Perceived Prevention	PV1	I am knowledgeable about disaster preventive measures set by local government units.	[25]
	PV2	I will join flood disaster preparedness activities conducted by my community.	[61]
	PV3	I will educate myself about the potential risks and effects of flood disasters.	[62]
	PV4	I seek information on how to prepare for flood disasters.	[62]
	PV5	My school educates me about disaster prevention and preparedness.
	PV6	My school informs me about possible flood disasters in the near future.

was used to identify the factors that influence flood disaster preparedness and mitigation in flood-prone areas in the Philippines, with the integration of Protection Motivation Theory (PMT) and Theory of Planned Behavior (TPB). The SAQ covers the following sections: (1) Demographic information including age bracket, gender, employment status, monthly income/allowance, highest and regional location, (2) Risk Perception, (3) Media, (4) Personal Experience, (5), Perceived Severity, (6) Perceived Vulnerability, (7) Attitude Toward the Behavior, (8) Social Norms, (9) Perceived Behavioral Control, (10) Intention to Follow, (11) Adapted Behavior, and (12) Perceived Prevention. Sections 2 to 12 contain the latent constructs, measured through a 5-point Likert scale agreement assessment with the range of strongly disagree (1), disagree (2), neutral (3), agree (4), and strongly agree (5).

3.3. Structural Equation Modelling (SEM)

Structural equation modeling (SEM) is acknowledged as a powerful tool that assists in analyzing and translating the relationships between multiple variables in a series of equations [63]. SEM has the ability to examine a series of dependent relationships simultaneously, and it is convenient for testing theories that can be represented by multiple equations that involve dependence relationships. Therefore, the use of the SEM technique would be suitable for acknowledging the factors affecting flood disaster preparedness and mitigation. Additionally, SEM can be used to guarantee the integrity of the results acquired by highlighting essential data required by the study’s results [64]. According to Zigde and Tsegaye [65], multivariate analysis such as SEM can assist in simplifying and organizing vast datasets to provide relevant perceptions. The SEM was considered to be suitable to be used in the study due to the research being involved in determining the elements influencing flood catastrophe preparedness and mitigation in flood-prone locations in the Philippines. It was also stated by Rao et al. [66] that multivariate analysis techniques can be used to study the relationship between latent variables like SEM.

Relating to the studies that used SEM in different aspects of human factors and consumer behavior, this study employed the use of AMOS v24 in assessing the causal relationship presented in the conceptual framework. A CB-SEM method was employed, similar to other studies [64,66], which considered a maximum likelihood estimation method. After several iterations setting a 95% confidence on the SEM runs, modification indices were applied for the final SEM and model fit validity.

4. Results

Figure 2 shows the initial SEM model for the factors affecting flood disaster preparedness and mitigation in flood-prone areas in the Philippines. It was determined that several hypotheses were insignificant, such as Risk perception to Perceived vulnerability (Hypothesis 1), Media to Perceived vulnerability (Hypothesis 4), Perceived vulnerability to Attitude toward to behavior (H10), and Perceived vulnerability to Social norms (Hypothesis 11). These hypotheses were removed due to having p-values greater than 0.05 [53]. In addition, Prasetyo et al. [48] suggested that a 0.50 threshold for the indicators using AMOS under CB-SEM should be used to determine significance. Thus, all the items with relationships drawn in broken lines are indicative of non-significance and were removed and re-run for the final SEM [48,52].

Shown in Figure 3 is the final SEM where the insignificant hypotheses were removed with improved model fit using modification indices [48]. The initial and final factor loading results are presented in

Table 3. Descriptive statistics results.

	Item	Mean	StDev	Variance	Factor Loading
					Initial	Final
Risk Perception	RP1	2.833	1.216	1.478	0.382	-
	RP2	3.721	1.010	1.020	0.879	0.866
	RP3	3.811	1.008	1.016	0.854	0.863
	RP4	3.200	1.108	1.227	0.194	-
	RP5	3.509	0.975	0.951	0.883	0.887
	RP6	4.246	0.858	0.737	0.390	-
Media	M1	4.037	0.863	0.745	0.551	0.663
	M2	4.128	1.012	1.025	0.802	0.803
	M3	4.130	1.005	1.011	0.887	0.897
	M4	4.283	0.940	0.884	0.752	0.772
Personal Experience	PE1	2.552	1.205	1.453	0.579	0.581
	PE2	3.483	1.201	1.443	0.396	-
	PE3	3.762	1.157	1.339	0.831	0.833
	PE4	3.859	1.108	1.228	0.915	0.916
	PE5	3.857	1.020	1.040	0.849	0.853
Perceived Severity	PS1	3.919	0.865	0.747	0.729	0.778
	PS2	4.352	0.835	0.697	0.890	0.898
	PS3	4.277	0.853	0.728	0.928	0.932
	PS4	4.420	0.816	0.665	0.425	-
Perceived Vulnerability	PV1	2.906	1.219	1.487	0.774	0.772
	PV2	2.963	1.292	1.670	0.825	0.826
	PV3	3.481	1.339	1.793	0.681	0.685
	PV4	3.354	1.254	1.572	0.741	0.741
	PV5	3.059	1.143	1.308	0.335	-
	PV6	3.045	1.169	1.366	0.363	-
Attitude toward the Behavior	ATB1	3.460	0.940	0.883	0.804	0.809
	ATB2	2.835	1.196	1.429	0.842	0.845
	ATB3	3.385	1.076	1.159	0.721	0.729
Social Norms	SN1	3.835	1.041	1.083	0.472	-
	SN2	3.949	0.877	0.769	0.665	0.665
	SN3	3.664	1.044	1.090	0.694	0.702
	SN4	3.821	0.931	0.868	0.749	0.753
Perceived Behavioral Control	PBC1	3.531	1.062	1.128	0.880	0.901
	PBC2	3.641	0.949	0.900	0.790	0.802
	PBC3	3.639	0.959	0.920	0.789	0.806
	PBC4	3.419	1.059	1.122	0.720	0.741
Adapted Behavior	AB1	3.316	1.124	1.264	0.172	-
	AB2	3.723	1.018	1.035	0.621	0.763
	AB3	3.312	1.208	1.459	0.669	0.712
	AB4	3.360	1.192	1.420	0.580	0.697
Intention to Follow	ITF1	2.780	1.281	1.640	0.683	0.698
	ITF2	3.189	1.194	1.425	0.662	0.692
	ITF3	3.961	0.971	0.943	0.754	0.726
	ITF4	2.664	1.281	1.641	0.708	0.715
	ITF5	4.090	1.014	1.027	0.748	0.753
Perceived Prevention	PP1	3.538	1.049	1.099	0.703	0.734
	PP2	3.417	1.096	1.200	0.699	0.701
	PP3	4.218	0.829	0.687	0.712	0.718
	PP4	4.110	0.885	0.783	0.704	0.717
	PP5	3.916	1.108	1.227	0.645	0.658
	PP6	3.864	1.085	1.176	0.710	0.732

with the descriptive statistic results for each latent variable and construct.

Table 4. Model Fit.

Goodness of Fit Measures	Parameter Estimates	Minimum Cutoff	Suggested by
Incremental Fit Index (IFI)	0.871	>0.80	Gefen et al. [67]
Tucker Lewis Index (TLI)	0.888	>0.80	Gefen et al. [67]
Comparative Fit Index (CFI)	0.887	>0.80	Gefen et al. [67]
Goodness of Fit Index (GFI)	0.855	>0.80	Gefen et al. [67]
Adjusted Goodness of Fit Index (AGFI)	0.824	>0.80	Gefen et al. [67]
Room Mean Square Error of Approximation (RMSEA)	0.055	<0.07	Steiger [68]

presents the model fit of the final SEM based on the indices.

As stated by Gefen et al. [67], the minimum value for a good model fit is 0.80. The values of the Incremental Fit Index (0.871), Tucker Lewis Index (0.888), Comparative Fit Index (0.887), Goodness of Fit Index (0.855), and Adjusted Goodness of Fit Index (0.824) were greater than or equal to 0.80, as presented in Table 4. Steiger [68] suggested that the Room Mean Square Error of Approximation should be less than 0.07, whereas the parameter estimated was 0.055, which indicates a good model fit.

Table 5. Composite reliability.

Factor	Cronbach’s Alpha	Average	Composite Reliability
Risk Perception	0.793	0.772	0.910
Media	0.835	0.621	0.866
Personal Experience	0.860	0.649	0.878
Perceived Severity	0.763	0.760	0.904
Perceived Vulnerability	0.839	0.574	0.843
Attitude toward the Behavior	0.749	0.633	0.838
Social Norms	0.740	0.501	0.750
Perceived Behavioral Control	0.801	0.663	0.887
Adapted Behavior	0.707	0.525	0.768
Intention to Follow	0.786	0.514	0.841
Perceived Prevention	0.814	0.505	0.859

shows the validity and reliability results for each variable and construct used in this study. Included in this test are the Cronbach α (CA), Average Variance Extracted (AVE), and Composite Reliability (CR). Ursachi et al. [69] stated that an alpha of 0.6 to 0.7 indicates an acceptable level of reliability, while higher presents a very good level. The results in Table 5 shows that the Cronbach’s Alpha values are within the acceptable range, which justifies the internal validity and reliability of the model.

Appendix A demonstrates the indicators’ total, direct, and indirect effects. Direct effects pertain to the impact or effect of the independent variable on the dependent variable when a mediating variable is present in the mode. These direct effects are present when no mediator is needed between variables for them to have an effect [70]. On the contrary, indirect effects indicate how a variable has an influence on a dependent variable through a mediator variable. If there are no mediator variables present between variables, no indirect effect will be present [70]. While the summation of the direct and indirect effects represents the total effects, all variables have a significant effect on each other (Appendix A).

5. Discussions

The disasters brought about by the frequent occurrences of flood disasters in flood-prone areas in the Philippines could be prepared for by implementing mitigation and preparedness measures. This research aimed to discover the factors that affect flood disaster preparedness and mitigation of individuals residing in flood-prone areas in the country by integrating the Protection Motivation Theory and the Theory of Planned Behavior assessed using Structural Equation Modelling (SEM). This statistical analysis technique was used to determine the interrelationship among the latent variables, which are risk perception (RP), media (M), personal experience (PE), perceived severity (PS), perceived vulnerability (PV), attitude toward the behavior (ATB), social norms (SN), perceived behavioral control (PBC), adapted behavior (AB), intention to follow (ITF), and perceived prevention (PP). This study will contribute to the few studies that the country has on flood disaster preparedness and mitigation, which can also be utilized as a basis to mandate future flood disaster preparedness measures.

The SEM results showed that RP has a significant direct effect on PS (β: 0.333; p = 0.001). Since the country is surrounded by sea waters, it is common knowledge to Filipinos that severe flood disasters may occur in the country [71]. However, individuals whose household size is 1 to 4 people, which is around 44.7% of the respondents, were said to hesitate to leave their houses despite perceiving the severity of a possible flood event. In accordance, the RP indicators suggested that those respondents can assess possible risks and aftermath, assess situations, and are willing to protect and take action. According to a study by Fothergill [72], those who have fewer household members hesitate to leave their homes because they are anxious about the effects that floods may leave on their homes. Aligning with the findings of this study, people would mitigate and have plans to prepare for natural hazards if they see the possible risks and outcomes. This is aligned with the intention to prepare for earthquake with Ong et al. [53]. Interestingly, people indicated that they cannot prepare by themselves for a flood disaster and have trouble trusting the evacuation facilities. This presents why RP has an indirect effect towards the behavioral domains, ITSF, AB, and PP. This is a note to consider among local government officials when it comes to the security evacuation centers provided for those affected by natural hazards. Since evident results showed significant direct effect, government officials can capitalize on the intention to follow latent variables among citizens for flood disaster response, practice, and mitigation plans.

Additionally, M presented a direct positive effect on PS (β: 0.461; p = 0.002). With the rise of social media in this generation, news outlets also began to utilize this to their advantage. This provided people with much faster news dissemination, which is essential, especially during disasters [20]. Since 80.7% of the respondents are Generation Z (ages 10 to 25 years old), who use social media at least 4 h a day, on average [73], many of the respondents spend most of their time on social media, which includes news regarding natural hazards happening worldwide. Twitter, a social media platform, was reported to have identified 10,000 flood events due to their users posting about them on said platform [43]. This relates to the presented significant indicators, such as respondents being able to filter relevant news and track natural hazards easily, use social media for communication purposes, and find relevant real news. Moreover, social media was also used as a tool for communication during flood disasters [21]. In accordance, the study of Gizzi and Potenza [74] showed that in the days immediately following the occurrence of natural hazards such as earthquakes or floods, people rapidly increase online activity in seeking information on preparedness actions. Thus, monitoring online behavior with Big Data can be useful for putting into the field well-timed and geographically targeted information and communication action plans by stakeholders. Since respondents were able to present the ability to capitalize on the rise of M in the current generation, their behavioral factors could be affected (as evident in the indirect effect), leading to a positive AB, ITF, and PP.

The results also showed that one’s PE had a significant impact on the PS (β: 0.467; p = 0.002) and PV (β: 0.651; p = 0.002) of flood disasters. Indicators like level of alertness and preparedness are found to be contributing factors to one’s perceived severity of flood disasters [25]. Being an archipelagic country, the Philippines experience several typhoons, which lead to floods [25]. Because of this, many Filipinos are alert and prepared when calamities, precisely flood disasters, strike [75]. Their PE showed how resiliency played a key role in their actions [76]. Similarly, with the knowledge applied from school-based education [77], adults and even the younger generations are more aware, leading to a positive indirect effect on ITF, PP, and AB.

The results also showed that PS has a significant effect on ATB (β: 0.721; p = 0.002), SN (β: 0.675; p = 0.002), and PBC (β: 0.739; p = 0.003). A person’s perception of flood severity has a direct effect on their hazard-related anxiety due to their personal experience. On the other hand, though personal experiences contribute to anxiety, according to Kuang [78], these experiences contribute to flood resilience as well. With these flood experiences, a person’s knowledge becomes broader; hence, they become more resilient in applying their knowledge to preventing disaster effects. Additionally, gender plays a factor in perceiving the severity of a disaster. Women are observed to be less confident in disasters; however, they have a better perception of preparing for natural hazards [42]. It was mentioned that women demonstrate a deeper understanding of flood events. With 56.2% of the respondents being female, this strengthens the claim of Cvetkovic et al. [42] that females are more likely to have preparedness measures than males.

PV resulted in having a negative relationship with PBC (β: -0.229; p = 0.002) and positive effect on SN (β: 116; p = 0.018). It could be considered that Filipinos are certain in their knowledge of preparing and mitigating the effects of flood disasters and are highly likely to perceive that they are not vulnerable to said disasters, especially when others are apparently influential, may be part of the disastrous event, or may be affected by the natural hazards’ aftermath [48,77]. They may also have gained trust from their local government units, since it was mentioned by Papagiannaki [79] that individuals who have gained knowledge and trust from government units become more confident in their ability to prepare and mitigate disastrous effects. This justifies the indirect effects found in the results under PV.

ATB has a positive direct impact on the latent variable AB (β: 0.346; p = 0.003). Most respondents answered that they have previously experienced flood disasters, and their attitude toward floods has also affected their preparedness. A person’s judgment on their ability to cope with the effects of a disaster reflects their actions or behaviors to prevent further damage caused by the disaster [24]. Hence, one’s notion of the degree of behavior should act on their coping behavior to disasters, justifying the indirect effects on the behavioral domains, AB, ITF, and PP.

Furthermore, SN has a positive influence on an individual’s AB (β: 0.373; p = 0.002) and indirect effects on TIF (β: 0.354; p = 0.001) and PP (β: 0.300; p = 0.001). According to Legros and Cislaghi [31], social norms have a role in action-oriented behavior and decision-making, which also influence one’s well-being and AB. Botzen et al. [54] expounded on the idea that people who live in neighborhoods that apply flood mitigation measures are most likely to follow the same behavior. Additionally, Ong et al. [53] stated that through social norms, individuals feel accountable for informing and preparing themselves for possible flood disasters. Since social norms are standards set by society that greatly help a neighborhood to mitigate flood disaster effects [62], a mitigating action made by an individual will contribute to the preparedness of a community.

The results show that one’s PBC directly affects their AB (β: 0.247; p = 0.002) and has a significant indirect effect on ITF and PP. In a study by Heidenrich et al. [57], it was mentioned that if an individual feels confident with their knowledge and ability to follow precautionary measures against disasters, they will be able to adapt to the behaviors to prevent and mitigate the damages flood disasters may bring—similar to the significant item measures found in the results of this study. Some flood mitigation and preparedness measures include Filipinos planning ahead of their commute to work for those who are in the working class [37] and government units offering safety measures to cater to those who are vulnerable to the effects of the said disaster [38].

The latent variable AB showed a significant effect on ITF (β: 0.949; p = 0.002). This means that Filipinos who have previously experienced devastating flood disasters and suffered physically or mentally adopt more behaviors to adapt to said disasters [28]. In relation to the location of the respondents, 48.1% of them reside in NCR, which indicates that several respondents have previously experienced disastrous floods. Hence, they are more likely to have adaptive behaviors, which often include preparing emergency supplies and items and flood- or water-proofing their homes in case of floods. Relating to the findings of this study, people will have ITF and PP when RP, M, and/or PE are evident, leading to an effect on PV over PS, then ATB over SN, over PBC.

Lastly, ITF presented a direct relationship with PP (β: 0.847; p = 0.002). This positive relationship between these two latent variables means that Filipinos are more likely to abide by government-mandated directives if they are asked to evacuate their homes. According to Kurata et al. [23], if Filipinos participate in community preparedness activities and workshops, this will increase their intention to follow government directives in order to prevent and mitigate damages from flood disasters. Similarly, different related studies in the Philippines showed that people are trusting and believe in the mitigation plans, protocols, and effectiveness of local government units, which should be promoted [53,77,78,80].

There has been an increase in typhoons entering the Philippine Area of Responsibility (PAR) compared to other foreign countries. It was reported by the Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA) that eight to nine typhoons cross the Philippines annually.

Table 6. Average number of typhoons that hit the three major islands from 2018 to 2022.

Island	Number of Typhoons
Luzon	5
Visayas	3.8
Mindanao	1.8

summarizes the average number of typhoons that made landfall on the three major islands in the country in the years 2018 to 2022 [81,82].

A proper segregation of the flood hazards and geographical mapping of respondents is presented in Figure 4. Similar to the distribution in the greatest number of typhoon hazards, the collected responses were mostly in Luzon, regions 1–5, and NCR, followed by Visayas, regions 5–8, and Mindanao as region 12. When it comes to the ages, the SEM presented that there is a significant inverse direct effect on PP as a controlled variable. Results have indicated that as age increases, PP decreases. This may be due to resiliency among Filipino citizens, as explained by several studies [41,78]. It was explained among the studies that Filipinos have the tendency to rely on experience, leading to least perception of risk or severity as time progresses. This study argues that proper mitigation and planning for natural hazards should still be considered.

As seen, most of the respondents are of younger generations who can either finish or attend college. As explained in the studies [53,78], higher perception of PP will be evident in younger generations who are able to finish senior high school, since part of the newly developed curriculum in 2016 considered Disaster Risk Reduction Management in the Philippines. This showed how the current generation has higher PP in relation to natural hazards indirectly, as well from AB and IF. Thus, it could be deduced from the findings that age and educational attainment does affect responses to natural hazards among Filipinos [83].

6. Conclusions

One of the natural hazards that happens repeatedly is flooding, which is caused by typhoons, monsoons, and heavy rainfall [1]. Studies say that catastrophic disasters can be avoided with preparedness and proper mitigation actions [15,77]. The purpose of this research is to identify the elements that influence flood disaster preparedness and mitigation in humans, especially those residing in flood-prone areas. The study’s findings will give additional data on the factors that affect flood disaster preparedness, which is a lifeline in flood disaster events.

These factors, risk perception (RP), media (M), personal experience (PE), perceived severity (PS), perceived vulnerability (PV), attitude toward the behavior (ATB), social norms (SN), perceived behavioral control (PBC), adapted behavior (AB), intention to follow (ITF), and perceived prevention (PP) were integrated using the Theory of Planned Behavior (TPB) and Protection Motivation Theory (PMT) and afterward analyzed using a structural equation modeling (SEM) approach. The results from this research show that the latent variables—intention to follow, perceived severity, personal experience, media, attitude toward the behavior, social norms, perceived behavioral control, perceived vulnerability, and risk perception—were determined to be factors that significantly affected the flood disaster preparedness and mitigation of individuals. The latent variable adapted behavior is determined to have the most significance in flood disaster preparedness and mitigation.

The study offers a solid model and data for specialists to assess natural hazards, specifically floods, and to create the necessary preventive measures to mitigate possible severe damages of floods from catastrophic typhoons. Lastly, the SEM structure can be expanded and used in flood preparedness, and it is applicable to other similar natural hazards.

6.1. Theoretical Contribution

The paper contributes to studies under the disaster risk reduction category in the Philippines. Being able to extend the frameworks of PMT and TPB aside from its integration with specific factors such as risk perception, media, and personal experience brought to light other factors that assess flood disaster preparedness and mitigation in flood-prone areas in the Philippines. In an attempt to measure the preparedness and mitigation, this study presented the geographical map that segregates the regions distinctly, which can aid in assessing flood-prone areas. The study utilized structural equation modeling (SEM) and determined the relationship between the latent variables based on the Theory of Planned Behavior (TPB) and the Protection Motivation Theory (PMT). Through the literature reviews and results from this research the latent variables—intention to follow, perceived severity, personal experience, media, attitude toward the behavior, social norms, perceived behavioral control, perceived vulnerability, and risk perception—were determined to be potential factors that affect the flood disaster preparedness and mitigation of individuals. This paper also determined that adapted behavior was the latent variable that had the most significance in flood disaster preparedness and mitigation.

6.2. Practical Implications

Through the results of this study, academicians can further determine the factors that have an effect on flood disaster preparedness, which can improve disaster risk management by the local government units (LGU) in the Philippines. It would specifically help the LGU in identifying the areas of need and recognizing the relationship between conditions and outcomes. This would also help them pinpoint which policies to evaluate and compare their performance each time. It is also common for LGUs to focus more on recovery and response, rather than doing more on the preparedness phase. The existing laws and policies that the Philippines has so much desire for are to be implemented; however, there is less emphasis on community efforts. Republic Act (RA) 10121 (Philippine National Disaster Risk Reduction and Management Act) was passed due to the 2009 Typhoon Ketsana. Due to flood damage and the loss of lives brought by the super typhoon, the RA 10121 was implemented in an attempt to thematize resilience among Filipino citizens [84]. The implementation of the law is lacking, which makes it difficult for the LGUs and NDRRMC to make the right preparations and mitigations. It has also been recognized by the law that there should be a need for communities and public efforts to reduce disaster risk. It is also in the law that a program must be built to solve the weaknesses of the LGUs and government agencies like the NDRRMC.

The majority of people do not take action until flooding or disaster has already happened. If Filipinos were exposed to more preparedness and activities, it would prevent and mitigate damage from imminent floods. Filipinos with past experiences with floods are more likely to adopt more behaviors that would allow them to adapt better in disasters [28]. Based on this study, an individual’s adapted behavior is a significant variable in their intention to follow. A person’s adaptive behavior is influenced by these factors; thus, providing people with the necessary skills is essential in changing their adapted behavior in floods. Kurata et al. [25] stated that a person’s behavior is directly correlated with behavioral intentions. To change this habit and mindset among the people themselves, behavioral change is required. To change a person’s behavior, it is also important to be aware of the practical implications of their actions.

6.3. Limitations and Future Research

Based on the respondent’s descriptive statistics, researchers should reach a broader audience, as respondents in this study are predominantly 15 to 24 years old (80.1%), have a personal monthly income less than PhP 10,957 (64.2%), have single as their marital status (86.1%), and are college undergraduates (58.2%). Those who are in poverty or are informal settlers should also be prioritized, as they tend to reside in flood-prone areas. They suffer the most due to their poor living conditions, along with how flood exacerbates poverty [77]. Since the current state of the Philippines still has implications of the COVID-19 pandemic due to the late response, the broad reach of demographics was not achieved but provides areas for future research.

The study was able to reach respondents in 12 regions out of the 17 regions in the Philippines. There were no respondents from Region VI Western Visayas, Region IX Zamboanga Peninsula, Region XIII CARAGA, CAR Cordillera Administrative Region, and BARMM Bangsamoro Autonomous Region in Muslim Mindanao. Since flood-prone areas all over the Philippines are addressed, it would be ideal for the number of respondents in every region or major island to be an approximately equal amount. In addition, it is suggested to perform clustering techniques on the different demographic sections to provide insights on the protection preparation on flood disaster in the Philippines. This way, researchers and the government may have a wider spread of information on the need for mitigation and disaster preventive plans.

To contribute to and continue further studies on the Philippines’ flood disaster preparedness and mitigation, the researchers have identified areas where further research and discussion are warranted. (1) A more comprehensive understanding of how adapted behavior (AB) is the most significant variable, to which higher order analysis such as machine learning may be applied. (2) Determine if and how flood disaster preparedness and mitigation in the Philippines constantly improves. (3) Utilize the Hyogo Framework to assess the Philippines’ disaster preparedness, as the framework is said to drive significant progress in development and legislation for disaster risk reduction.