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Kybernetes ◽  
2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhen-Yu Chen

PurposeMost epidemic transmission forecasting methods can only provide deterministic outputs. This study aims to show that probabilistic forecasting, in contrast, is suitable for stochastic demand modeling and emergency medical resource planning under uncertainty.Design/methodology/approachTwo probabilistic forecasting methods, i.e. quantile regression convolutional neural network and kernel density estimation, are combined to provide the conditional quantiles and conditional densities of infected populations. The value of probabilistic forecasting in improving decision performances and controlling decision risks is investigated by an empirical study on the emergency medical resource planning for the COVID-19 pandemic.FindingsThe managerial implications obtained from the empirical results include (1) the optimization models using the conditional quantile or the point forecasting result obtain better results than those using the conditional density; (2) for sufficient resources, decision-makers' risk preferences can be incorporated to make tradeoffs between the possible surpluses and shortages of resources in the emergency medical resource planning at different quantile levels; and (3) for scarce resources, the differences in emergency medical resource planning at different quantile levels greatly decrease or disappear because of the existing of forecasting errors and supply quantity constraints.Originality/valueVery few studies concern probabilistic epidemic transmission forecasting methods, and this is the first attempt to incorporate deep learning methods into a two-phase framework for data-driven emergency medical resource planning under uncertainty. Moreover, the findings from the empirical results are valuable to select a suitable forecasting method and design an efficient emergency medical resource plan.


Kybernetes ◽  
2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yang Li ◽  
Jiaze Li ◽  
Qi Fan ◽  
Zhihong Wang

PurposeThe teenager community is the most affected community by cybercrime in the COVID-19 era. Increasing social networks and facilitating teenager access to the Internet have increased the probability of cybercrimes. On the other hand, entertainment such as mobile and computer games is top-rated among teenagers. Teenagers' tendency to cybercrime may be influenced by individual, parent, social, economic and political factors. Studying the impact of social networks, mobile games and parents' religious attitudes on teenagers' tendency to cybercrimes in the COVID-19 era is the primary goal of this paper.Design/methodology/approachThe outbreak of COVID-19 caused a considerable change in the world and the lifestyle of all people. Information and Communication Technology (ICT) was also affected by the special conditions of this virus. Changes in ICT and rapid access to it have empowered individuals and organizations, and people have increased civic participation and interaction through ICT. However, the outbreak of COVID-19 has created new challenges for the government and citizens and may cause new crimes. Cybercrime is a type of crime that occurs in a cyber environment. These crimes range from invasions of privacy to crimes in which the offender vaguely paralyzes the macroeconomic. In this research, 265 students of high schools and universities are used for collecting data by utilizing a survey. Measuring actions have been done in all surveys employing a Likert scale. The causal pattern is assessed through a constructional equation modeling procedure to study the scheme's validity and reliability.FindingsThe outcomes have indicated that social networks have no significant relationship with teenagers' tendency to cybercrimes in the COVID-19 era. Mobile games have a mild effect on teenagers' tendency to cybercrimes in the COVID-19 era, and parents' religious attitudes significantly impact teenagers' tendency to cybercrimes in the COVID-19 era.Research limitations/implicationsCurrent research also has some restrictions that must be noticed in assessing the outcomes. First, sample research was selected from high schools and universities in one city. So, the size of the model is small, and the generalization of results is limited. Second, this research may have ignored other variables that affect the tendency of teenagers' to cybercrime. Future researchers intend to investigate the parents' upbringing system's impact on teenager's trend to cybercrime in the COVID-19 era. Future research can also examine practical factors such as parental upbringing, attitudes toward technology development and virtual addiction in the COVID-19 era.Originality/valueIn this study, teenagers' tendency to cybercrimes in the COVID-19 era is investigated, and a procedure is applied depending on a practical occasion. This article's offered sample provides a perfect framework for influencing parents' social networks, mobile games and religious attitudes on teenagers' tendency to cybercrimes in the COVID-19 era.


Kybernetes ◽  
2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hanieh Shambayati ◽  
Mohsen Shafiei Nikabadi ◽  
Seyed Mohammad Ali Khatami Firouzabadi ◽  
Mohammad Rahmanimanesh ◽  
Sara Saberi

PurposeSupply chains (SCs) have been growingly virtualized in response to the market challenges and opportunities that are presented by new and cost-effective internet-based technologies today. This paper designed a virtual closed-loop supply chain (VCLSC) network based on multiperiod, multiproduct and by using the Internet of Things (IoT). The purpose of the paper is the optimization of the VCLSC network.Design/methodology/approachThe proposed model considers the maximization of profit. For this purpose, costs related to virtualization such as security, energy consumption, recall and IoT facilities along with the usual costs of the SC are considered in the model. Due to real-world demand fluctuations, in this model, demand is considered fuzzy. Finally, the problem is solved using the Grey Wolf algorithm and Firefly algorithm. A numerical example and sensitivity analysis on the main parameters of the model are used to describe the importance and applicability of the developed model.FindingsThe findings showed that the Firefly algorithm performed better and identified more profit for the SC in each period. Also, the results of the sensitivity analysis using the IoT in a VCLSC showed that the profit of the virtual supply chain (VSC) is higher compared to not using IoT due to tracking defective parts and identifying reversible products. In proposed model, chain members can help improve chain operations by tracking raw materials and products, delivering products faster and with higher quality to customers, bringing a new level of SC efficiency to industries. As a result, VSCs can be controlled, programmed and optimized remotely over the Internet based on virtual objects rather than direct observation.Originality/valueThere are limited researches on designing and optimizing the VCLSC network. This study is one of the first studies that optimize the VSC networks considering minimization of virtual costs and maximization of profits. In most researches, the theory of VSC and its advantages have been described, while in this research, mathematical optimization and modeling of the VSC have been done, and it has been tried to apply SC virtualization using the IoT. Considering virtual costs in VSC optimization is another originality of this research. Also, considering the uncertainty in the SC brings the issue closer to the real world. In this study, virtualization costs including security, recall and energy consumption in SC optimization are considered.HighlightsInvestigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).


Kybernetes ◽  
2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sherbaz Khan ◽  
Aamir Rashid ◽  
Rizwana Rasheed ◽  
Noor Aina Amirah

PurposeThe purpose of this study is to present a complete framework that defines the link between choices and decision criteria based on existing research on digital influencers (DIs) connected to consumer purchase intentions. The primary goal of this article is to assess the effect of DIs on customer purchase intentions via the creation of an integrated knowledge-based system (KBS).Design/methodology/approachThe suggested KBS is based on the fuzzy analytic hierarchy process (AHP), which creates a link between DI elements and their overall effect on consumer purchase intentions.Findings With the help of a KBS, the performance of DIs may be evaluated. It demonstrates the link between choices connected to factors and decision criteria of various variables, demonstrating the beneficial effect of DIs in molding customer purchase intentions in the organic skincare industry.Practical implicationsThe proposed KBS would aid marketing managers and decision makers in assessing the effect of DIs on customer purchase intentions. This research would also give decision makers with extensive information on influencer marketing and crucial elements that have a significant effect on customer purchase intentions.Originality/valueThis is the first research to employ the fuzzy AHP methodology and KBS in relation to influencers' effect. No prior research has targeted the organic skincare industry to assess the effect of Internet influencers on consumer purchase intentions. Furthermore, the KBS offers a holistic and complete way to studying influencers' effect on cost per impression (CPI) by establishing a linkage between choices and decision criteria.


Kybernetes ◽  
2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
AHmet Hakan Özkan

PurposeThe aim of this study was to survey the relationships between organizational justice perceptions and turnover intention by using meta-analysis.Design/methodology/approachEighty-three correlation values were used. Comprehensive Meta-Analysis Software (CMA) was used to analyze the collected studies.FindingsHeterogeneity and publication bias of each data set was tested. Each data was heterogeneous and included no publication bias. The results suggested that the effect size of distributive justice on turnover intention is −0.396, interactional justice on turnover intention is −0.341, interpersonal justice on turnover intention is −0.361, informational justice on turnover intention is −0.358, procedural justice on turnover intention is −0.369 and overall organizational justice on turnover intention is −0.436. Region was a moderator for the relationship between distributive justice and turnover intention.Originality/valueThe results of this study can provide guidance to the future researchers. Moreover, the managers can use these results for the implementation of organizational strategies and policies.


Kybernetes ◽  
2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Min Zhao ◽  
Kamran Rabiei

PurposeThe present study is descriptive research in terms of purpose, descriptive analysis in terms of nature and cross-sectional research in terms of time. The study’s statistical population includes all employees and managers of the China City Organization selected as sample members using random sampling method and Krejcie table of 242 people. The questionnaire was modified and revised based on the goals, tasks and mission of the target organization to collect information. In data analysis, due to the normality of data distribution, the structural equation modeling method is used to evaluate the causal model, reliability and validity of the measurement model. Evaluation and validation of the model are done through the structural equation model. Questionnaire-based model and data are analyzed using Smart PLS 3.0. The main purpose of this study is to assess the feasibility of implementing the human resource payroll management system based on cloud computing technology.Design/methodology/approachNew technologies require innovative approaches for creating valuable opportunities in an organization to integrate the physical flows of goods and services and financial information. Today, cloud computing is an emerging mechanism for high-level computing as a storage system. It is used to connect to network hosts, infrastructure and applications and provide reliable services. Due to advances in this field, cloud computing is used to perform operations related to human resources. The role, importance and application of cloud computing in human resource management, such as reducing the cost of hardware and information software in hiring, job planning, employee selection, employee socialization, payroll, employee performance appraisal, rewards, etc., is raised. This way, human resource management teams can easily view resumes, sort candidates and observe and analyze their performance. Cloud computing is effective in implementing human resource payroll management systems. Therefore, the primary purpose of this study is to assess the feasibility of implementing the human resource payroll management system based on cloud computing technology.FindingsTesting the research hypotheses shows that the dimension desirability of ability and acceptance is provided in dimensions related to the minimum conditions required to implement cloud computing technology in the organization. For this reason, the feasibility of implementing the systems based on cloud computing in companies must be considered.Research limitations/implicationsThis study also has some limitations that need to be considered in evaluating the results. The study is limited to one region. It cannot be assured that the factors examined in other areas are effective. The research design for this study is a cross-sectional study. It represents the static relationship between the variables. Since cross-sectional data from variable relationships are taken at a single point in time, they are collected in other periods. As a proposal, future researchers intend to investigate the impact of Enterprise Resource Planning (ERP) systems based on cloud computing.Practical implicationsThe research also includes companies, departments and individuals associated with systems based on cloud computing.Originality/valueIn this paper, the feasibility of implementing the human resource payroll management system based on cloud computing is pointed out, and the approach to resolve the problem is applied to a practical example. The presented model in this article provides a complete framework to investigate the feasibility of implementing the human resource payroll management system based on cloud computing.


Kybernetes ◽  
2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yu-Hui Wang ◽  
Guan-Yu Lin

PurposeThe purposes of this paper are (1) to explore the overall development of AI technologies and applications that have been demonstrated to be fundamentally important in the healthcare industry, and their related commercialized products and (2) to identify technologies with promise as the basis of useful applications and profitable products in the AI-healthcare domain.Design/methodology/approachThis study adopts a technology-driven technology roadmap approach, combined with natural language processing (NLP)-based patents analysis, to identify promising and potentially profitable existing AI technologies and products in the domain of AI healthcare.FindingsRobotics technology exhibits huge potential in surgical and diagnostics applications. Intuitive Surgical Inc., manufacturer of the Da Vinci robotic system and Ion robotic lung-biopsy system, dominates the robotics-assisted surgical and diagnostic fields. Diagnostics and medical imaging are particularly active fields for the application of AI, not only for analysis of CT and MRI scans, but also for image archiving and communications.Originality/valueThis study is a pioneering attempt to clarify the interrelationships of particular promising technologies for application and related products in the AI-healthcare domain. Its findings provide critical information about the patent activities of key incumbent actors, and thus offer important insights into recent and current technological and product developments in the emergent AI-healthcare sector.


Kybernetes ◽  
2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ahmad Latifian

PurposeBig data has posed problems for businesses, the Information Technology (IT) sector and the science community. The problems posed by big data can be effectively addressed using cloud computing and associated distributed computing technology. Cloud computing and big data are two significant past-year problems that allow high-efficiency and competitive computing tools to be delivered as IT services. The paper aims to examine the role of the cloud as a tool for managing big data in various aspects to help businesses.Design/methodology/approachThis paper delivers solutions in the cloud for storing, compressing, analyzing and processing big data. Hence, articles were divided into four categories: articles on big data storage, articles on big data processing, articles on analyzing and finally, articles on data compression in cloud computing. This article is based on a systematic literature review. Also, it is based on a review of 19 published papers on big data.FindingsFrom the results, it can be inferred that cloud computing technology has features that can be useful for big data management. Challenging issues are raised in each section. For example, in storing big data, privacy and security issues are challenging.Research limitations/implicationsThere were limitations to this systematic review. The first limitation is that only English articles were reviewed. Also, articles that matched the keywords were used. Finally, in this review, authoritative articles were reviewed, and slides and tutorials were avoided.Practical implicationsThe research presents new insight into the business value of cloud computing in interfirm collaborations.Originality/valuePrevious research has often examined other aspects of big data in the cloud. This article takes a new approach to the subject. It allows big data researchers to comprehend the various aspects of big data management in the cloud. In addition, setting an agenda for future research saves time and effort for readers searching for topics within big data.


Kybernetes ◽  
2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Abroon Qazi ◽  
Mecit Can Emre Simsekler ◽  
Steven Formaneck

PurposeThis paper aims to assess the impact of different drivers of country risk, including business environment, corruption, economic, environmental, financial, health and safety and political risks, on the country-level logistics performance.Design/methodology/approachThis study utilizes three datasets published by reputed international organizations, including the World Bank Group, AM Best and Global Risk Profile, to explore interactions among country risk drivers and the Logistics Performance Index (LPI) in a network setting. The LPI, published by the World Bank Group, is a composite measure of the country-level logistics performance. Using the three datasets, a Bayesian Belief Network (BBN) model is developed to investigate the relative importance of country risk drivers that influence logistics performance.FindingsThe results indicate a moderate to a strong correlation among individual risks and between individual risks and the LPI score. The financial risk significantly varies relative to the extreme states of the LPI score, whereas corruption risk and political risk are the most critical factors influencing the LPI score relative to their resilience and vulnerability potential, respectively.Originality/valueThis study has made two unique contributions to the literature on logistics performance assessment. First, to the best of the authors’ knowledge, this is the first study to establish associations between country risk drivers and country-level logistics performance in a probabilistic network setting. Second, a new BBN-based process has been proposed for logistics performance assessment and operationalized to help researchers and practitioners establish the relative importance of risk drivers influencing logistics performance. The key feature of the proposed process is adapting the BBN methodology to logistics performance assessment through the lens of risk analysis.


Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Harsh M. Shah ◽  
Bhaskar B. Gardas ◽  
Vaibhav S. Narwane ◽  
Hitansh S. Mehta

PurposeThis paper aims to conduct a systematic literature review of the research in the field of Artificial Intelligence (AI) and Big Data Analytics (BDA) in Supply Chain Risk Management (SCRM). Finally, future research directions in this field have been suggested.Design/methodology/approachThe papers were searched using a set of keywords in the SCOPUS database. These papers were filtered using the Title abstract keywords principle. Further, more papers were found using the forward-backward referencing method. The finalized papers were then classified into eight categories.FindingsThe previous papers in AI and BDA in SCRM were studied. These papers emphasized various modelling and application techniques for AI and BDA in making the supply chain (SC) more resilient. It was found that more research has been done into conceptual modelling rather than real-life applications. It was seen that the use of AI-based techniques and structural equation modelling was prominent.Practical implicationsAI and BDA help build the risk profile, which will guide the decision-makers and risk managers make their decisions quickly and more effectively, reducing the risks on the SC and making it resilient. Other than this, they can predict the risks in disasters, epidemics and any further disruption. They also help select the suppliers and location of the various elements of the SC to reduce the lead times.Originality/valueThe paper suggests various future research directions that fellow researchers can explore. None of the previous research examined the role of BDA and AI in SCRM.


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