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The purpose of this paper is to identify the initial emergency response time of fire fighting teams in Malaysia.

In an emergency incident time is of the essence, and the basic philosophy of an emergency response agency is to respond as quickly as possible to minimize the loss of life and property damage. In the current study, emergency response performance refers to team members' speed in responding to emergency situations, which was measured as the time taken for the team members to get to the fire truck from the waiting room in selected fire stations in Malaysia. The data collection period lasted for five months.

This study found that the overall average initial emergency response time was 84 seconds, while the overall average weighted initial emergency response time was 3.71 seconds per meter. The current study has demonstrated that the average initial emergency response performed by fire fighting teams in Malaysia is apparently better than that reported by previous studies by other emergency responders.

This paper presents empirical evidence of the initial emergency response time of fire fighters in Malaysia, by taking into account the distance traveled by the responders. As such, the performance measure obtained gives a meaningful indicator. The finding of the current study is then compared to emergency response performance by other emergency response agencies in other countries.

The purpose of the paper is to estimate the effect of rapid intervention vehicles (RIVs)’ impact on fire department (FDs)' response time.

The study employed multivariate regression analysis of changes in response times before and after introducing RIVs into the FD task force, controlling for expected travel time. Response time analysis based on the database of all emergency fire responses in Norway from 2016 to 2021 was carried out.

Introducing RIVs into the FDs' task force reduced response times by 53 s on average for every call where an RIV is being deployed, given an average driving length of an emergency call of 6.4 kilometers. The response time is reduced to approximately 37 s independently of driving length, and this effect increases with 2.5 s per km.

Precise estimation of the expected reduced response time was calculated by introducing the first RIV into the FD's task force based on advanced statistical analysis on complete emergency register data. The analysis shows that RIVs have a positive impact on response time in both urban and rural areas, while particularly strong for urban areas.

The aim of this study is to develop and redesign the Mobile Panic Button UI (PB1) application as an emergency notification service, as well as conduct a simulation on the use of the new version of the application.

This study used the operational research design with a theoretical research framework that followed input, process and output. Primary data were collected through observation, measurement and interview while the secondary data were obtained from literature review. The first step of the analysis was input analysis that included problem identification on the existing Panic Button as input, or PB 1 in this study. This was followed by the analysis of the planned second version of the Panic Button (PB2) development, starting from problem identification to trial and error testing and evaluation of the results of the simulation of seven types of emergency events that involved users, emergency response officers and experts.

The study shows that the first version of PB1, developed in and used since 2017 at Universitas Indonesia, still had several weaknesses, particularly in its inability to provide adequate information. Only 30% of the standards for a mobile emergency application were met by PB1, which affected the performance of emergency responses. This was one of the reasons why the new version of Panic Button UI (PB2) was developed. The new features in PB2 comprise the inclusion of features for collecting information on user's name and mobile phone number, emergency category options, victim information, photo/voice information, description on the nature of the emergency (text), location selection, emergency notification delivery, notification delivery popup and emergency notification delivery to the emergency response team (ERT) officer. The time needed for using the second version of the Panic Button UI mobile application is 20 s faster than the previous one. PB2 can accelerate response time and response action time; improve response accuracy; facilitate the emergency notification process; and facilitate emergency communication.

Various notification alert systems have been developed in many countries. However, there is a lack of information in Indonesia, especially in educational setting. This study is the first study on a notification alert system application applied in the university. Emergency response is critical due to the big impact of disasters. This study will inform the stakeholders or users, particularly those in educational institution on how to implement mobile app–based emergency response notification systems.

Retailers receiving items from a manufacturer carry inventory to meet customer demand. As items are sold, a retailer orders new items to replenish the inventory. Once an order is placed, there is a time taken for the items to be delivered to the retailer. This time is the manufacturing response time. It includes processing, production, and delivery times. These different components of time can result in response times that are long and uncertain. This paper develops a queueing model for analysing how manufacturing response time affects the inventory needed at retailers to meet demand. The model accounts for variability in response times and allows for products to be delivered to a retailer in a different sequence than they were ordered. Simple equations are derived for the average inventory in terms of demand and response time parameters. The equations show how shortening average response time can substantially reduce retailer inventory.

This study examines the impact of response time on user experience for mobile applications and considers the moderating influence of gender and network environment on this relationship.

An experiment was conducted with 50 young adults to evaluate their user experience of a mobile application that simulates variations in network environment and response time. User experience was evaluated based on the three constituent dimensions of tolerance, acceptance, and satisfaction.

Analytical results demonstrate that response time not only adversely affects user experience of mobile applications, but that this effect is not homogeneous across the three dimensions of tolerance, acceptance and satisfaction. The findings also illustrate that gender moderates the effect of response time on user experience, however, the negative influence is more salient for males than females, which is opposite to our hypothesis. The joint moderating influence of gender and network environment turned out to be partly significant.

By illuminating users' tolerance, acceptance, and satisfaction with varied response times, findings from this study can inform the design of mobile applications such that desired levels of user experience can be assured with minimum resources.

Although response time has been hailed as a key determinant of user experience for desktop applications, there is a paucity of studies that have investigated the impact of response time on user experience for mobile applications. Furthermore, prior research on response time neglects the multi-dimensional nature of user experience. This study bridges the above mentioned knowledge gaps by delineating user experience into its constituent dimensions and clarifying the effects of response time on each of these dimensions.

The conclusions on the importance of corporate response timing to a crisis have remained inconsistent. Some studies suggest that active response may reduce negative impacts, whereas managers argue that issuing official response frustrates stakeholders and thus decreases the firm value. The purpose of this paper is to investigate the role of external media in the response timing strategy and the consequent stock market reaction.

Based on 130 corporate crises that befell publicly listed firms in China from 2007 to 2014, this paper uses the Baidu News Search Engine and Chinese Lexical Analysis System to construct the variables of the media characteristics. A structural equation model is established to test the hypotheses.

The results of this paper suggest that media coverage drives response timing after a crisis. Although an official response is a burden for firms, the timing strategy has multidimensional benefits including effectively alleviating negative effects (defined as buffering effects) and repairing the market (defined as restoring effects). Moreover, the buffering effects of response timing are stronger when completeness of response is low.

This study mainly contributes to crisis communication literature by introducing the role of media in prompting managers to make timing decisions. The findings of this study provide empirical support for the importance of timing response strategy.

The lack of studies about the response time of emergency medical service during the coronavirus disease 2019 (COVID-19) pandemic in a dense city of a developing country has triggered this study to explore the factors contributing to a high response time of ambulance service to reach patients in need. An evaluation of contributing factors to the response time is necessary to guide decision-makers in keeping a high service level of emergency medical service.

This research employed an agent-based modeling approach with input parameters from interviews with emergency medical service staff in Bandung city, Indonesia. The agent-based model is established to evaluate the relevant contribution of the factors to response time reduction using several scenarios.

According to agent-based simulation, four factors contribute to the response time: the process of preparing crew and ambulance during the pandemic, coverage area, traffic density and crew responsiveness. Among these factors, the preparation process during the pandemic and coverage area significantly contributed to the response time, while the traffic density and crew responsiveness were less significant. The preparation process is closely related to the safety procedure in handling patients during the COVID-19 pandemic and normal time. The recommended coverage area for maintaining a low response time is 5 km, equivalent to six local subdistricts.

This study has explored the factors contributing to emergency medical response time. The insignificant contribution of the traffic density showed that citizens, in general, have high awareness and compliance to traffic priority regulation, so crew responsiveness in handling ambulances is an irrelevant factor. This study might have different contributing factors for less dense population areas and focuses on public emergency medical services provided by the local government.

The local government must provide additional funding to cover additional investment for ambulance, crew and administration for the new emergency service deployment point. Exercising an efficient process in ambulance and crew preparation is mandatory for each emergency deployment point.

This study evaluates the contributing factors of emergency medical response time in the pandemic and normal situation by qualitative analysis and agent-based simulation. The performance comparison in terms of medical response time before and after COVID-19 through agent-based simulation is valuable for decision-makers to reduce the impact of COVID-19.

The purpose of this paper is to analyze how response time in a laboratory experiment on bank runs affects withdrawal decisions.

In the authors’ setup, the bank has no fundamental problems, depositors decide sequentially whether to keep the money in the bank or to withdraw, and they may observe previous decisions depending on the information structure. The authors consider two levels of difficulty of decision-making conditional on the presence of strategic dominance and strategic uncertainty. The authors hypothesize that the more difficult the decision, the longer is the response time, and the predictive power of response time depends on difficulty.

The authors find that response time is longer in information sets with strategic uncertainty compared to those without (as expected), but the authors do not find such relationship when considering strategic dominance (contrary to the hypothesis). Response time correlates negatively with optimal decisions in information sets with a dominant strategy (contrary to the expectation) and also when decisions are obvious in the absence of strategic uncertainty (in line with the hypothesis). When there is strategic uncertainty, the authors find suggestive evidence that response time predicts optimal decisions.

Being a laboratory experiment, it is questionable if depositors in real life behave similarly (external validity).

Since episodes of bank runs are characterized by strategic uncertainty, the result that under strategic uncertainty, longer response time leads to better decisions suggests that suspension of convertibility is a useful tool to curb banking panics.

To the best of authors’ knowledge, this is the first study concerning the relationship between response time and the optimality of decisions in a bank-run game.

Careless responders are respondents who lack the motivation to answer survey questions accurately. Empirical findings can be significantly distorted when some respondents devote insufficient effort to the survey task, and researchers therefore attempt to identify such respondents. Many measures of careless responding have been suggested in the literature, but researchers frequently struggle with the selection and appropriate use of the available methods. This chapter offers a classification of existing measures of careless responding along two dimensions and presents a conceptual discussion of their relative strengths and weaknesses. An empirical study demonstrates how the various measures can be used to identify careless responders and how these measures are related to each other.

The primary objective of the current study is to examine the impact that the size of a police department and workload on a department have on response time. Secondarily, the authors look at the effect that incident-level factors such as the severity of a call for service (CFS) and community-level factors such as ethnic diversity have on police response time.

The current study examined CFSs data collected over a three-year period and organizational information from 26 police departments in Northeast Texas, as well as community-level information. In order to measure the effect of organizational variables, community factors and incident-level variables on police response time, the authors employed the use of hierarchical linear models (HLMs).

The results of hierarchical linear modeling indicated that incident-level factors and police department size are significant predictors of response time.

There are two primary limitations: first, there were a lack of available organizational structure correlates such as age and differentiation. Second, the primary data set had a significant number of incomplete or repeating cases, thereby limiting the accuracy of the current study’s analysis.

The most unique aspect of this manuscript is that it examines how organizational factors affect police response time. Numerous studies analyzed determinants of police response time such as incident- and community-level factors, but the importance of organizational factors has not been analyzed.