Open access peer-reviewed chapter

# The IDEA Model as a Conceptual Framework for Designing Earthquake Early Warning (EEW) Messages Distributed via Mobile Phone Apps

By Deanna D. Sellnow, Lucile M. Jones, Timothy L. Sellnow, Patric Spence, Derek R. Lane and Nigel Haarstad

Submitted: November 28th 2018Reviewed: March 1st 2019Published: March 30th 2019

DOI: 10.5772/intechopen.85557

## Abstract

Short response time available in the event of a major earthquake poses unique challenges for earthquake early warning (EEW). Mobile phone apps may be one way to deliver such messages effectively. In this two-phase study, several hundred participants were first randomly assigned to one of eight experimental conditions. Results of phase one afforded researchers the ability to reduce the number of conditions to four. Phase two consisted of five experimental conditions. In each condition, a 10 second EEW was delivered via a phone app. The four treatment conditions were designed according to elements of the IDEA model. The control condition was based on the actual ShakeAlert EEW computer program message being used by emergency managers across the US west coast at the time. Results of this experiment revealed that EEW messages designed according to the IDEA model were more effective in producing desired learning outcomes than the ShakeAlert control message. Thus, the IDEA model may provide an effective content framework for those choosing to develop such apps for EEW.

### Keywords

• IDEA model
• risk communication
• crisis communication
• disaster warnings
• earthquake early warning
• communication and technology

## 1. Introduction

Effective earthquake early warning messages can empower target populations to take appropriate actions for self-protection and, ultimately, save lives. The communication challenges facing those who wish to design warning messages involve both content and access. Content focuses on gaining attention and providing appropriate instructions for self-protection. Access depends on sending the messages through a channel or channels and a medium or media that can be retrieved quickly and easily. A team of researchers completed a project designed to develop such content and access. The project was based on previous warning message testing research. Specifically, researchers attempted to apply the IDEA model to create brief, easily accessible earthquake early warning messages via a mobile phone app.

The IDEA model for effective instructional risk and crisis communication is an acronym that stands for internalization, distribution, explanation, and action [1]. According to the IDEA model, such messages ought to include appeals to internalization (e.g., proximity, personal relevance, impact, timeliness), be distributed over multiple channels deemed appropriate based on crisis type and target audience(s), and offer cogent explanations about what is happening. These explanations should be offered by credible sources and the scientific information provided in them be both accurate and translated intelligibly for the target population(s). These messages also must include specific action steps receivers are to take (or not take) for self-protection [2, 3, 4, 5, 6, 7]. The following paragraphs describe the message design and testing project processes, results, and conclusions based on the timeline under which it unfolded.

## 2. The IDEA model message design and testing experiment

The design and testing process occurred in two phases. Thus, this section first describes the study design process followed by the results of the two-phase experiment. It closes with a discussion of the results as they may inform the design of effective EEW messages delivered via phone apps.

### 2.1 Designing the study

To launch the project, a multidisciplinary group comprised of seismologists, instructional risk and crisis communication scientists, graphic artists, and emergency managers from the US west coast states met in Pasadena, California to participate in a 3-day design storm focused on earthquake early warning messaging. This design storm was essentially a synergistic brainstorming session to formulate an ecologically valid plan based on a broad cross-section of expertise represented in crisis communication and earthquake science that would inform earthquake early warning message design.

Ultimately, the group agreed that message content distributed via a phone app would likely be a predominant interface for US west coast residents. Thus, message content (both visual and aural) would be designed for a phone app. The group also agreed that the content would need to be developed based on social science crisis communication best practices research.

Message content would address internalization components as follows. To test proximity, some conditions will include a map and others will not. To test timeliness, the conditions will include a countdown to when the strong shaking is expected to occur. Timeliness would also be tested by providing no more than 10 seconds for the entire message. Personal relevance would be addressed by focusing on “very strong shaking” (i.e., “7” or higher Intensity level shaking).

Message content would address explanation components as follows. To address source credibility, Dr. Lucy Jones’ voice was recorded and applied as she is a known and credible earthquake expert among many throughout the US west coast states. The accurate science provided by seismologists was translated into simple, easily comprehended language. Also, intensity level was selected rather than magnitude because intensity is directly related to very strong shaking that can harm individuals that do not enact the appropriate actions for self-protection. Finally, some conditions used a verbal message–very strong sharking—and others a numerical message—“7”—to signal the kind of shaking to occur. This allowed researchers to test for potential lack of understanding regarding what “7” might mean. Existing research suggest that less numerate people—those that lack the ability to process mathematical concepts—tend to trust verbal risk information that they can comprehend more than numeric information that may be unintelligible to them and, consequently, make poorer decisions based on numerical data than highly numerate people [8]. Thus, it seemed critical to test intensity comprehension based on numeric versus verbal reporting.

Message content would address action components by a visual graphic accompanied by a verbal message—Drop! Cover! Hold on!—reinforced orally by a speaker saying “Drop, take cover, hold on.” All experts in the design storm agreed that all conditions testing high intensity earthquake early warning message should include this specific action statement in some way. Thus, all treatment conditions included this message.

The graphic artists created eight visual representations of a smart phone app screen, which the instructional risk and crisis team would test during the fall semester. The team would create an online survey to collect responses to the various versions and measure their effectiveness based on affective, cognitive, and behavioral learning outcomes. A snowball sample of participants would be invited via Lucy Jones’ Facebook page and the Shakeout website. The survey collected quantitative and qualitative data and employed a mixed methods analysis.

### 2.2 Message testing: phase one

#### 2.3.1 Perceived quality of the app

A series of stepwise regression analyses were conducted to examine the research question about perceived quality of the app. The single item asking about the quality of the app used a five-point Likert type response scale (1 = very effective to 5 = not effective). Overall, 75% of the participants across conditions rated the app as “effective” or “very effective” and only 2% rated the app as “not effective.” On the first block, demographic variables were entered in order to account for any variance attributable to respondent characteristics. These included sex, age, race/ethnicity, and income. The second predictor block included these variables, as well as experimental condition. The examination focused on significant models and predictors, as well as potential improvements based on the addition of experimental condition.

The results for the first predictor block indicate a significant model, F(4, 223) = 6.775, p < 0.001. R2 = 0.108. Of the demographic variables only sex β = −249 p < 0.000, and age β = −175 p < 0.01 were predictive of ratings of app quality. When experimental condition was added to the predictor block a significant model was also produced, F(5, 222) = 4.32, p < 0.001, R2 = 0.112. However, the change in variance accounted for was not significant ΛR2 = 0.004. Of the variables in the predictor block, only sex β = −245 p < 0.000, and age β = −176 p < 0.01 were predictive of ratings of app quality.

A t-test was conducted for the variables of sex and overall quality across conditions. Women (M = 1.73 SD = 0.81) were more likely than men (M = 2.14, SD = 1.04) to rate the app as being of high quality t(2) = 3.592, p < .001. Sex differences in perceptions of app quality were then broken down by each condition. Differences were found for condition 2, where women (M = 1.61, SD = 1.12) reported higher perceptions of app quality than men (M = 2.30. SD = .74) t(2) = 2.696, p < 0.01, and condition 5 where women (M = 1.70, SD = 0.65) reported higher perceptions of quality than men (M = 2.19, SD = 1.01) t(2) = 2.190, p < 0.05.

Perhaps most important here is that participants in all treatment conditions rated the quality of the app as high. Since all treatment conditions used similar content based on the IDEA model (i.e., alert sound, oral and visual countdown, intensity level, map, actionable instructions), it seems the appropriate content is being included. Moreover, a thematic analysis of the open-ended responses revealed that those viewing the control (ShakeAlert) condition were “overwhelmed by the visuals” and wanted to see and hear directions to “duck, cover, and hold on.” These themes suggest that (a) too much information, although accurate, can defeat the purpose of the warning and (b) specific action steps need to be included. In addition to perceived quality of the app, the researchers sought to learn more regarding numerical versus verbal intensity displays, the effect of the map in location cognition (proximity), and behavioral intentions to take appropriate self-protective actions.

#### 2.3.2 Intensity

Key findings from this round of message testing regarding intensity are as follows. First, there were no significant differences among conditions regarding intensity. However, an exploration of descriptive statistics shed additional light on this issue. When asked “how important is it to know the kind of shaking,” 76–87% reported it as very important across all conditions. Moreover, 77–85% of the respondents across conditions answered correctly (i.e., 10 seconds or less) when asked when the shaking would begin.

Important findings emerged when asked what kind of shaking would occur. It is encouraging to note that 77–93% of the respondents reported correctly that very strong shaking was going to occur. The researchers placed a screen shot before entering the survey that summarized the meaning of the numerical intensity numbers. When respondents that viewed the verbal intensity display were asked about the numerical intensity level (8), only 15 and 22.4% recalled the correct number. Of the respondents that viewed the numerical intensity display, 69 and 80% recalled the correct number. Of the respondents that viewed the control (ShakeAlert) message, only 35.5% recalled the correct number. This low percentage may be impacted by the amount of detailed information being displayed in the control message. So much information may be difficult to process in 10 seconds or less and, thus, may result in misunderstanding.

Subsequently, when asked how well they understand the meaning of intensity level numbers, 48.4 and 38.8% of those viewing the verbal display marked “very well.” Respondents that viewed the numerical intensity display reported knowledge comprehension of “very well” at 56.7 and 56.5%. Those viewing the control (ShakeAlert) message reported knowing the meaning very well at 45.9%. These results suggest the verbal intensity display is more meaningful than the numerical display. These results also suggest that displaying both (as in the control ShakeAlert message) appears to be too much information to process accurately in a short amount of time.

#### 2.3.3 Location

All conditions included a map identifying where the shaking was going to occur. There were no significant differences among the conditions regarding the importance of the map or for accurate location identification. Across conditions, 74–92% reported a map as “important” or “very important.” A somewhat troubling finding, however, was that when asked where the shaking was going to occur, only 33–55% answered correctly (Los Angeles area) across conditions. When the researchers drilled down to include only participants currently living in southern California, the results improved slightly among the four treatment conditions (64–74% correct). However, only 29% of the respondents that viewed the control (ShakeAlert) message answered correctly. Moreover, when asked how helpful the visual images were in conveying information about location, only 27.9–50% said “very helpful” across conditions. However, in all four treatment conditions, respondents reported more preference for the visual images (M = 1.90, SD = 1.82) than those in the control condition (M = 2.26, SD = 1.30) t(2) = −2.106 = p < 0.05. Moreover, a thematic analysis of the open-ended comments revealed a desire for a simple map that merely showing a familiar city with a bullseye target or location flag would be more helpful than one showing both the epicenter and location where shaking will occur. Taken together, these results suggest that a simple map highlighting the location may be more effective than a detailed one showing lots of information.

#### 2.3.4 Behavioral intentions

A series of stepwise regression analyses were conducted to examine the research question regarding behavioral intentions. The composite measures were used to assess perceptions of behavioral intentions. The measure for behavioral intentions used nine items with a response scale of 1 = “Very Unlikely” to 5 = “Very Likely.” On the first block, demographic variables were entered in order to account for any variance attributable to respondent characteristics. These included sex, age, race/ethnicity, and income. The second block added experimental condition to these possible predictor variables. The analyses focused on significant models and predictors, as well as possible improvement to the model based on the addition of experimental condition.

The results for the first predictor block did not indicate a significant model, F(4, 227) = 0.989, p = n.s. R2 = 0.017. None of the demographic variables were predictive of behavioral intentions. When experimental condition was added to the predictor block the model did not improve, F(5, 229) = 0.788, p = n.s., R2 = 0.017. None of the variables in the predictor block were predictive of behavioral intentions.

The fact that no significant model stood out as a better predictor for behavioral intentions combined with the descriptive statistics suggest that the including the IDEA model components as we did in each condition may be effective for earthquake early warning messages delivered via a smart phone app. Although the means reported are encouraging, the fact that the pretest self-efficacy (M = 4.44) also may point to a respondent pool comprised of members of a disaster sub-culture that is already pre-disposed to taking appropriate actions for self-protection.

## 3. Conclusions

Several promising conclusions may be drawn from these two rounds of message design and testing. First, a phone APP can be designed in ways that employ the IDEA elements of effective instructional risk and crisis messages for earthquake early warnings in 10 seconds or less. Second, the elements of the IDEA model do appear to positively influence affective (perceived value/importance), cognitive (comprehension), and behavioral (efficacy and intention) learning outcomes.

Also based on these message testing results, however, more honing of some particulars are still warranted. For example, with regard to internalization, the design of the map (proximity) needs to be simplified to ensure accurate comprehension of location. Regarding explanation, it appears that verbal intensity displays are more effective than numerical displays unless a comprehensive educational campaign could be conducted to teach users what the different numbers mean.

The sample for both rounds of message testing was not representative of the entire population in southern California. Additional message testing targeting more representative demographic diversity and marginalized populations is warranted in order to be certain about ultimately launching the most effective warning app.

## How to cite and reference

### Cite this chapter Copy to clipboard

Deanna D. Sellnow, Lucile M. Jones, Timothy L. Sellnow, Patric Spence, Derek R. Lane and Nigel Haarstad (March 30th 2019). The IDEA Model as a Conceptual Framework for Designing Earthquake Early Warning (EEW) Messages Distributed via Mobile Phone Apps, Earthquakes - Impact, Community Vulnerability and Resilience, Jaime Santos-Reyes, IntechOpen, DOI: 10.5772/intechopen.85557. Available from:

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