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This study aims to review the stages of the traditional disaster timeline, propose an extended version of this timeline and discuss the disaster strategies relevant to the different stages of the extended timeline.

An extensive review of the existing literature was made to discuss the need for an extended version of the conventional disaster timeline and to explain the differences between the various disaster management strategies. The research approach was based on theoretical and practical reasoning underpinned by the literature.

The proposed extended disaster timeline allows better allocation of a wider range of management strategies. Successful disaster management depends on prioritisation of efforts and the use of the right strategy(s) at the right time: before, during and after an incident.

This study provides a better conceptualisation of the disaster stages and corresponding strategies. It clarifies the role of each strategy, thus linking it more effectively with the disaster timeline. Subsequently, this study is expected to improve decision-making associated with the disaster management process. In the end, it is expected to help transforming the conventional disaster timeline into a more practical one that is result-oriented more than only being a conceptual model.

Disaster management strategies are used interchangeably very often in the literature. A few attempts were made to capture multiple strategies in one study to demonstrate what constitutes effective disaster management without mixing irrelevant strategies with the different disaster stages.

The purpose of the study is to analyze the impact of the Publication Timeline, i.e. Submission to First Decision and Submission to Acceptance on Journal Metrics, i.e. Impact Factor (IF) and 5-year IF.

Data related to the IF and 5-year IF were retrieved from Clarivate Analytics’ Journal Citation Report 2020. The Publication Timeline of each journal was ascertained through their respective websites. To attain the model fit summary, an analysis of variance (ANOVA) test was performed. Regression analysis was also performed on the models using SPSS 21 software to ascertain the nature and degree of impact the Publication Timeline (Submission to First Decision and Submission to Acceptance) has on Journal Metrics (IF and 5-year IF).

Submission to First Decision has a significant inverse relationship with both the IF and 5-year IF, whereas Submission to Acceptance has a significant direct relation with 5-year IF and an inverse but insignificant relationship with IF.

Journals published by Springer Nature and of multidisciplinary nature have been considered for the study. Only those journals were selected that provided the information regarding the Publication Timeline, whereas those which did not provide the same, were excluded. However, new insights can be revealed if the journals published by different publishers and belonging to one particular discipline are studied.

The study helps to ascertain the impact of the Publication Timeline on the Impact Metrics of the journals. It can help the authors select the journals as their publishing venues considering the Publication Timeline. Publishers can also be benefitted from the findings of this study since improvisations and modifications in their Publication Timelines can positively influence the impact metrics of their respective journals.

The study attempts to measure the impact of the Publication Timeline on Journal Metrics using cross-sectional secondary data, by performing regression analysis. Though various studies have examined the influence of the Publication Timeline on the IF using correlation analysis, to the best of the authors’ knowledge, this study is the first of its kind to use regression analysis to check the relation, as well as the degree of impact the Publication Timeline, has on Journal Metrics.

In this study, the process and effects of integrating an innovative technology were examined in one U.S. history teacher’s classroom. Specifically, the impact on the teacher and the students from using digital timelines to understand history are presented. Recommendations from this study include: (a) the importance of examining and including a variety of documents to present multiple perspectives and to portray different voices in a digital timeline, (b) the need for the teacher to articulate and describe the how and the why of the digital timeline assignments as it relates to understanding historical content knowledge, and (c) the need for students to think critically about the dates, events, and figures included in their timelines.

This case study aims to demonstrate how the Timeline Theatre, a non‐profit organization that has won awards for managerial excellence and artistic achievement, has implemented both an experience‐driven strategy (its conscious choice) and an ongoing commitment to co‐creation of unique value through experiences (its values‐based approach). By adopting both, TimeLine has achieved significant artistic and organizational growth.

The case traces the non‐profit's strategy development and its implementation of two innovative strategic management concepts.

The paper finds that TimeLine's strategy and core values are both important factors underpinning its run of success.

The case provides a model for other organizations that want to experiment with these strategic concepts.

The ideas of co‐creation of value and experience‐based strategy are mutually reinforcing and become more powerful taken in combination. In settings where experience‐based offerings involve the customer directly, opportunities for co‐creation become abundant. Further, application of the co‐creation model can lead directly to “new engagement experiences of value to the customer.”

The purpose of the study is to evaluate the relationship of Journal Publication Timeline (submission to first decision and submission to final decision) with various Journal Metrics (citing half-life, article influence score, the immediacy index, the acceptance rate, the impact factor (IF), five years IF, Eigenfactor and cited half-life) of top 600 journals retrieved from Journal Citation Report (JCR) 2020 under the tag, Elsevier Unified.

Top 600 journals in the decreasing order of the IFs under the tag, “Elsevier Unified” were retrieved from JCR 2020 of Clarivate Analytics. Information about “Journal Metrics” was ascertained using “Customized Service” of JCR, while information about the “Publication Timeline” of each journal was obtained using Elsevier's “Journal Insights Service.” It was found that only 177 journals provided the complete information regarding the “Publication Timeline” and hence considered for the study. Descriptive statistics and correlation analysis was conducted to test the different hypotheses.

It was found that submission to first decision has a significant relationship with the immediacy index, citing half-life and the acceptance rate. Submission to final decision has a significant relationship with Journal Impact Factor (JIF), the immediacy index, Eigenfactor, citing half-life and the acceptance rate.

The study will provide the authors with sound and valuable information to support their selection of journals. Inferences in light of fluctuations in the scholarly communication process in terms of Publication Timelines and Journal Metrics can be deeply understood with the aid of the current study's findings. What considerations authors have to take before submitting their papers is the main implication of the study. Journal administrators can also benefit from the findings of the current study as it can help recruit and manage reviewers, which will ensure a successful publication timeline.

The study correlates Publication Timeline Indicators with Journal Metrics Indicators using secondary cross-sectional data. Though most previous studies only examine the relationship of the Publication Timeline with the Journal Impact Factor (JIF), there is very scarce literature that deciphers the influence of Publication Timeline indicators on different Journal Metrics indicators (including JIF).

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-02-2022-0108.

The purpose of this paper is to outline a variety of related methods for helping with criminal (cold) case investigations. Despite the best efforts of police investigations, many cases around the world run out of leads and go cold. While many police departments around the world have developed specialist groups and task forces, academics have also been developing new methods that can assist with investigations.

Cold cases, by their very nature, typically comprise incomplete data sets that many traditional statistical methods are not suited to. Groups of researchers have therefore developed temporal, dynamic analysis methods to offer new insights into criminal investigations. These methods are combined into a timeline toolkit and are outlined in the current paper.

Methods from the timeline toolkit have already been successfully applied to many cold cases, turning them back into current cases. In this paper, two real-world cold cases are analysed with methods from the timeline toolkit to provide examples of how these methods can be applied in further cold cases.

Methods from the timeline toolkit provide a novel approach to investigating current and cold cases. This review provides academics and practitioners with a guide to begin using and developing these methods and forming successful collaborations with police departments and cold case task forces. The methods are also suitable for wider groups and to use in their investigations.

This chapter explores the different ways we share our research. The ways in which we are productive, again, help shape the tools in which productivity occurs. Archiving, storing, and sharing are crucial within the field of digital humanities and offer interesting perspectives on how we as humans share information with one another. The focus in this chapter is also on specific tools like timelines, e-portfolios, and the new generation of presentation applications.

Completing Right-of-Way (ROW) acquisition process on schedule is critical to avoid delays and cost overruns on transportation projects. However, transportation agencies face challenges in accurately forecasting ROW acquisition timelines in the early stage of projects due to complex nature of acquisition process and limited design information. There is a need of improving accuracy of estimating ROW acquisition duration during the early phase of project development and quantitatively identifying risk factors affecting the duration.

The quantitative research methodology used to develop the forecasting model includes an ensemble algorithm based on decision tree and adaptive boosting techniques. A dataset of Georgia Department of Transportation projects held from 2010 to 2019 is utilized to demonstrate building the forecasting model. Furthermore, sensitivity analysis is performed to identify critical drivers of ROW acquisition durations.

The forecasting model developed in this research achieves a high accuracy to predict ROW durations by explaining 74% of the variance in ROW acquisition durations using project features, which is outperforming single regression tree, multiple linear regression and support vector machine. Moreover, number of parcels, average cost estimation per parcel, length of projects, number of condemnations, number of relocations and type of work are found to be influential factors as drivers of ROW acquisition duration.

This research contributes to the state of knowledge in estimating ROW acquisition timeline through (1) developing a novel machine learning model to accurately estimate ROW acquisition timelines, and (2) identifying drivers (i.e. risk factors) of ROW acquisition durations. The findings of this research will provide transportation agencies with insights on how to improve practices in scheduling ROW acquisition process.

Data literacy – the ability to read, analyze, interpret, evaluate and argue with data and data visualizations – is an essential competency in social studies. This study aims to examine the degree to which US state standards require teachers to teach data literacy in social studies, addressing the questions: to what extent are US social studies teachers required to teach data literacy? If they are required to teach it, are they provided with guidance about competencies to address at each school or grade level and with respect to particular content?

The study used content analysis, using a variety of priori and emergent codes, to review social studies standards documents from all 50 states and the District of Columbia.

Findings indicate that although state standards suggest that data visualizations should play a role in social studies instruction, they provide poor guidance for a coherent, progressive and critical approach across grade levels.

Researchers currently know little about if and how teachers address data literacy in social studies education. This study provides a snapshot of guidance teachers across states are given for teaching data literacy, and by extension, the quality of data literacy instruction recommended for students across the USA.