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The COVID-19 pandemic is a new crisis in the world that caused many restrictions, from personal life to social and business. In this situation, the most vulnerable groups such as refugees who are living in the camps are faced with more serious problems. Therefore, a system dynamic approach has been developed to evaluate the effect of applying different scenarios to find out the best response to COVID-19 to improve refugees’ health and education.

The interaction of several health and education factors during an epidemic crisis among refugees leads to behavioral responses that consequently make the crisis control a complex problem. This research has developed an SD model based on the SIER model that responds to the public health and education system of Syrian refugees in Turkey affected by the COVID-19 virus and considered three policies of isolation, social distance/hygiene behavior and financial aid using the available data from various references.

The findings from the SD simulation results of applying three different policies identify that public health and education systems can increase much more by implementing the policy of social distance/hygiene behavior, and it has a significant impact on the control of the epidemic in comparison with the other two responses.

This paper contributes to humanitarian organizations, governments and refugees by discussing useful insights. Implementing the policy of social distance and hygiene behavior policies would help in a sharp reduction of death in refugees group. and public financial support has improved distance education during this pandemic.

The novel coronavirus disease 2019 (COVID-19) pandemic has presented unique challenges in terms of understanding its unique characteristics of transmission and predicting its spread. The purpose of this study is to present a simple, parsimonious and accurate model for forecasting mortality caused by COVID-19.

The presented Bass Model is compared it with several alternative existing models for forecasting the spread of COVID-19. This study calibrates the model for deaths for the period, March 21 to April 30 for the USA as a whole and as the US States of New York, California and West Virginia. The daily data from the COVID-19 Tracking Project has been used, which is a volunteer organization launched from The Atlantic. Every day, data is collected on testing and patient outcomes from all the 50 states, 5 territories and the District of Columbia. This data set is widely used by policymakers and scholars. The fit of the model (F-value and its significance, R-squared value) and the statistical significance of the variables (t-values) for each one of the four estimates are examined. This study also examines the forecast of deaths for a three-day period, May 1 to 3 for each one of the four estimates – US, and States of New York, California and West Virginia. Based on these metrics, the viability of the Bass Model is assessed. The dependent variable is the number of deaths, and the two independent variables are cumulative number of deaths and its squared value.

The findings of this paper show that compared to other forecasting methods, the Bass Model performs remarkably well. In fact, it may even be argued that the Bass Model does better with its forecast. The calibration of models for deaths in the USA, and States of New York, California and West Virginia are all found to be significant. The F values are large and the significance of the F values is low, that is, the probability that the model is wrong is very miniscule. The fit as measured by R-squared is also robust. Further, each of the two independent variables is highly significant in each of the four model calibrations. These forecasts also approximate the actual numbers reasonably well.

This study illustrates the applicability of the Bass Model to estimate the diffusion of COVID-19 with some preliminary but important empirical analyses. This study argues that while the more sophisticated models may produce slightly better estimates, the Bass model produces robust and reasonably accurate estimates given the extreme parsimony of the model. Future research may investigate applications of the Bass Model for pandemic management using additional variables and other theoretical lenses.

The Bass Model offers effective forecasting of mortality resulting from COVID-19 to help understand how the curve can be flattened, how hospital capacity could be overwhelmed and how fatality rates might climb based on time and geography in the upcoming weeks and months.

This paper demonstrates the efficacy of the Bass Model as a parsimonious, accessible and theory-based approach that can predict the mortality rates of COVID-19 with minimal data requirements, simple calibration and accessible decision calculus. For all these reasons, this paper recommends further and continued examination of the Bass Model as an instrument for forecasting COVID-19 (and other epidemic/pandemic) mortality and health resource requirements. As this paper has demonstrated, there is much promise in this model.

The purpose of this paper is to examine the role of perceived home and destination organizational culture characteristics and general satisfaction with the assignment as antecedents of expatriates’ withdrawal intentions.

Data were collected through a web survey of an international sample of expatriates with a broad representation of industries, organizations and countries of origin and destination.

The results indicate that home and destination organizational cultures affect expatriates’ withdrawal intentions, after controlling for demographics and national cultural differences, namely: home organizational culture has a stronger influence on withdrawal intentions from the organization, while host organizational culture affects withdrawal intentions from the assignment. Further, the relationship between host organizational culture and expatriates’ intentions to withdraw from the assignment is mediated by expatriates’ satisfaction with the assignment. Evidence was also found supporting a stronger and negative influence of the goal orientation dimension of organizational culture, thus suggesting that a collective orientation toward common business goals (i.e. solidarity) may help retain expatriates.

This study seeks to fill a gap in the literature by exploring the influence of organizational culture on expatriates’ withdrawal intentions, and the mediating role of expatriates’ satisfaction with the assignment, on that relationship.

This paper aims to address the need for responsive methodologies to investigate how information and communication technologies (ICTs) are used in non‐business and non‐corporate environments.

The paper presents a case study on developing an IT strategic plan in a community organisation using the process modelling and analysis methodology called “Co‐MAP”.

Co‐MAP as a methodology is significant in being a participatory, responsive, and non‐obtrusive tool to work with welfare workers in getting to articulate information, knowledge and technical issues for decision making.

The research provides a way of obtaining knowledge about structuring of social‐technical relationships in a welfare organisation through a sympathetic approach to its business and culture.

Co‐MAP could be fruitfully used in other organisations, though whether this needs an external facilitator to carry out the process and manage the complex data analysis process is a moot point.

The significance of this case study is that it develops a model for adaptation of how to research and represent data, information, and knowledge flows within a social services organisation, for which there are few other detailed case studies.

Crowdfunding (CF) is a digital-financial innovation that, bypassing credit crisis, bank system rigidities and constraints of the capital market, is allowing new ventures and established companies to get the needed funds to support innovations. After one decade of research, mainly focused on relations between variables and outcomes of the CF campaign, the literature shows methodological lacks about the study of its overall behavior. These reflect into a weak theoretical understanding and inconsistent managerial guidance, leading to a 27% success ratio of campaigns. To bridge this gap, this paper embraces a “complex system” perspective of the CF campaign, able to explore the system's behavior of a campaign over time, in light of its causal loop structure.

By adopting and following the document model building (DMB) methodology, a set of 26 variables and mutual causal relations modeled the system “Crowdfunding campaign” and a data set based on them and crafted to model the “Crowdfunding campaign” with a causal loop diagram. Finally, system archetypes have been used to link the causal loop structure with qualitative trends of CF's behavior (i.e. the raised capital over time).

The research brought to 26 variables making the system a “Crowdfunding campaign.” The variables influence each other, thus showing a set of feedback loops, whose structure determines the behavior of the CF campaign. The causal loop structure is traced back to three system archetypes, presiding the behavior in three stages of the campaign.

The value of this paper is both methodological and theoretical. First, the DMB methodology has been expanded and reinforced concerning previous applications; second, we carried out a causation analysis, unlike the common correlation analysis; further, we created a theoretical model of a “Crowdfunding Campaign” unlike the common empirical models built on CF platform's data.

The purpose of this study is to (re-)evaluate the explanatory power of the stressor–stress–strain model and its' current operationalization by examining the influence of general and interaction adjustment and the mediating effect of general satisfaction on expatriates' and spouses' intention to prematurely return from an assignment or overseas location. Though expatriates' premature return intention has been well examined in prior literature, this is the first study to focus on spouses' premature return intention from the expatriate's assignment.

To evaluate the hypotheses, a sample of 104 expatriates and a sample of 64 spouses were collected and analysed utilizing structural equation modeling.

The results show that adjustment, as the opposite of distress, is not a direct negative driver of expatriates' nor spouses' premature return intention. Instead, the findings underscore the relevance of the general satisfaction with the international assignment (IA) as a mediator for both expatriates and spouses, which emphasizes the importance of attitudinal factors in the model. Overall, the results indicate that adjustment, in particular interaction adjustment, might not be a timely measure of distress anymore.

In order to reduce expatriates' and spouses' premature return intention multinational corporations should aim at maximizing satisfaction levels during the IA. To achieve this, both should be included in the selection process prior to the IA to tailor support mechanisms to satisfy their expectations.

This study is the first to investigate the premature return intention from the expatriates' and spouses' perspectives, while (re-)evaluating the explanatory power of the stressor–stress–strain model at present.

As retailers have increasingly embraced an omnichannel retailing strategy, explaining and predicting the helpfulness of online review should consider both online-based and offline-based reviews. The paper aims to discuss this issue.

Based on the signaling theory, this study intends to examine the impacts of review-related and reviewer-related signals on review helpfulness in the context of omnichannel retailing. The proposed research model and corresponding hypotheses were tested by using negative binomial regression.

The results shown that both review-related (review rating and review sentiment strength) and reviewer-related (reviewer real name and reviewer expertise) signals positively affect review helpfulness. Contrary to the authors’ expectations, review length negatively affects review helpfulness. Specifically, when the review submitted from an omnichannel retailer’s offline channel, the positive impacts of reviewer real name on review helpfulness will be stronger, and the positive impacts of reviewer expertise on review helpfulness will be weaker.

Unlike many previous studies tend to explore the antecedents of review helpfulness in a single-channel setting, the study examined the factors that affect review helpfulness in an omnichannel retailing context.

Explores the state of the art in internal auditing risk assessment techniques by reviewing professional requirements as stated in Statement of Internal Auditing Standards (SIAS) No. 9 and then examining and discussing currently available risk assessment techniques. Models explored in the study include the traditional risk assessment model and those by Wilson and Randon; Patton, Evans and Lewis; Boritz; and Siers and Blyskal. The results of the study indicate that professional standards are being met by all of the risk assessment techniques examined but none of the techniques is perfect for all users. Indeed, each has at least one flaw which seriously compromises its usefulness. Empirical research comparing predicted areas of high risk with actual areas could be used to determine the robustness of these models.

Big data analytics allows researchers and industry practitioners to extract hidden patterns or discover new information and knowledge from big data. Although artificial intelligence (AI) is one of the emerging big data analytics techniques, hospitality and tourism literature has shown minimal efforts to process and analyze big hospitality data through AI. Thus, this study aims to develop and compare prediction models for review helpfulness using machine learning (ML) algorithms to analyze big restaurant data.

The study analyzed 1,483,858 restaurant reviews collected from Yelp.com. After a thorough literature review, the study identified and added to the prediction model 4 attributes containing 11 key determinants of review helpfulness. Four ML algorithms, namely, multivariate linear regression, random forest, support vector machine regression and extreme gradient boosting (XGBoost), were used to find a better prediction model for customer decision-making.

By comparing the performance metrics, the current study found that XGBoost was the best model to predict review helpfulness among selected popular ML algorithms. Results revealed that attributes regarding a reviewer’s credibility were fundamental factors determining a review’s helpfulness. Review helpfulness even valued credibility over ratings or linguistic contents such as sentiment and subjectivity.

The current study helps restaurant operators to attract customers by predicting review helpfulness through ML-based predictive modeling and presenting potential helpful reviews based on critical attributes including review, reviewer, restaurant and linguistic content. Using AI, online review platforms and restaurant websites can enhance customers’ attitude and purchase decision-making by reducing information overload and search cost and highlighting the most crucial review helpfulness features and user-friendly automated search results.

To the best of the authors’ knowledge, the current study is the first to develop a prediction model of review helpfulness and reveal essential factors for helpful reviews. Furthermore, the study presents a state-of-the-art ML model that surpasses the conventional models’ prediction accuracy. The findings will improve practitioners’ marketing strategies by focusing on factors that influence customers’ decision-making.

This study examines the determinants of medical crowdfunding performance. Drawing on signaling theory, the authors investigate how funding-related signals (funding goal and duration), story-related signals (text length, text sentiment, and use of first-person pronouns), and donor-related signals (donor identity disclosure) affect medical crowdfunding performance.

This study analyzed the data of 754 medical crowdfunding projects collected from the Qingsongchou platform in China to test the proposed model.

The empirical findings reveal that both funding goal and funding duration exhibit a U-shaped relationship with crowdfunding performance. Additionally, the authors find evidence that story text length and donor identity disclosure are positively related to crowdfunding performance, whereas the use of first-person pronouns is negatively related to crowdfunding performance.

This study extends the understanding of the determinants of medical crowdfunding performance through the signaling theory. Specifically, this study provides new insights into the roles of funding goal and funding duration in predicting medical crowdfunding performance and identifies several new predictors of crowdfunding performance, including the use of first-person pronouns in project story text and donor identity disclosure.