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The purpose of this paper is to improve the efficiency of accident management from the angle of reducing highway accident response times while taking into account total daily work hours.

The authors developed a patrol beat scheduling model, which is formulated as a chance-constrained optimization model, with the objective of minimizing the sum of officer work hours. Along with the model, a simulation program was also developed to help evaluate the effectiveness of the model-generated beat schedule in terms of response times.

This study concluded that, first, the current manually designed beat schedule could be improved should the National Highway Police Bureau adopt the proposed model. Second, the total daily work hours of the model-generated schedule at the confidence level of 100 percent were 64 hours, 21 hours less than the average work hours recorded in the 2006 data, or about an improvement of 24 percent. Should the model be adopted, not only response times will be improved, the 24 percent reduction in work hours could be translated into a cut in personnel cost.

The scheduling model and simulation program are both built upon one-year historical data whose accuracy and completeness is prerequisite.

The proposed model can be adopted by other public service agencies such as fire departments, or emergency service centers. By replacing the historical data used in the study with their own data, agencies can evaluate the efficiency of their existing schedule or generate various schedules based on institutional needs.

This model utilizes historical accident data to generate optimal beat schedule and evaluate the efficiency of such schedule. Similar models have not been found in other studies.

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.

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 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.

The purpose of this paper is to investigate whether the macroeconomic effects of government spending shocks vary with the degree of macroeconomic uncertainty.

The authors use quarterly US data from 1960 to 2017 and employ the Self-Exciting Interacted VAR (SEIVAR) to compute nonlinear generalized impulse response functions (GIRFs) to an orthogonalized government spending shock during tranquil and in uncertain times. The parsimonious design of the SEIVAR enables us to focus on extreme deciles of the uncertainty distribution and to control for the financing side of the government budget, monetary policy, financial frictions and consumer confidence.

Fiscal spending has positive output effects in tranquil times, but is contractionary during times of heightened macroeconomic uncertainty. The results indicate an important role of the endogenous response of macroeconomic uncertainty. Investigating different government spending purposes, only increases in research and development expenditures reduce uncertainty and boost output during uncertain times.

The authors contribute to the literature in using a method which allows to control for a large set of confounding factors and accounts for the uncertainty response.

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.

Given that the majority of firms are followers of pioneering firms into new, related product markets, managers of almost all firms can benefit from benchmarking their competitive responses to pioneering new product introductions. This paper examines the competitive responses of firms not only in terms of the time until their responding new product introduction, but also in terms of the firms’ preceding stages of competitive response: awareness, interest, and evaluation. For example, how long does – and should – it take a follower firm to become aware of a pioneering new product introduction? A general conceptual framework and basic methodology is proposed for firms to evaluate and benchmark their competitive responses. Follower firm responses to pioneering new low‐fat food product introductions in North America are examined and illustrate the opportunity for benchmarking firms’ competitive responses.