Acceptance Decisions Research Articles

Factors associated with decision making on COVID-19 vaccine acceptance among college students in South Carolina

ABSTRACT COVID-19 vaccination could be a promising approach in controlling the pandemic, but its success relies on the vaccine acceptance among various populations including young adults who are vulnerable to COVID-19 due to active lifestyle and perceived invulnerability. Vaccine acceptance decisions can be influenced by multiple factors and people may weigh these factors differently in decision making. The current study aimed to explore COVID-19 vaccine acceptance among college students in South Carolina and examine how they weigh these factors according to their COVID-19 vaccine acceptance levels (i.e. acceptance, hesitance, refusal). Online survey data were collected from 1062 college students in South Carolina between September and October 2020. Multinomial logistic regresssion was used to compare perceived importance of 12 factors affecting levels of vaccine acceptance, controlling for demographic variables. About 26.1% of participants reported they would definitely take COVID-19 vaccines when available. Compared to acceptance group, refusal and hesitance groups considered side effects and vaccine characteristics (e.g. where the vaccine is produced) as important. Hesitance group considered authoritative advice from school/college as important. Acceptance group considered authoritative advice from government/doctors and local availability of the vaccines and local availability of the vaccines as important. Our findings suggest relatively low vaccine acceptance among college students in South Carolina and different factors were considered in their vaccination decision according to their acceptance levels. Tailored vaccine promotion messages should address specific concerns among the refusal and hesitancy groups. Schools should attend to valid communication strategies in vaccine campaign since the hesitancy group considered school’s advice as important. College health educators also need to pay attention to the refusal group who do not value duration of protection or authoritative advice as much as their counterparts in vaccine decision making.

Sequential Procurement with Contractual and Experimental Learning

We study the design of sequential procurement strategies that integrate stochastic and strategic information. We consider a buyer who repeatedly demands a certain good and is unable to commit to long-term contracts. In each time period, the buyer makes a price offer to a seller who has private, persistent information regarding his or her cost and quality of provision. If the offer is accepted, the seller provides the good with a stochastic quality that is not contractible. Therefore, the buyer can learn from the (strategic) acceptance decisions taken by the seller and from evaluations of the (stochastic) quality delivered whenever a purchase occurs. Hence, the buyer not only faces a tradeoff between exploration and exploitation but also needs to decide how to explore: by facilitating quality experimentation or by strategically separating seller types. We characterize the perfect Bayesian equilibria of this sequential interaction and show that the buyer’s equilibrium strategy consists of a dynamic sequence of thresholds on his or her belief on the seller’s type. When only one seller type is more efficient than the buyer’s outside option, the buyer uses one form of information: either strategic or stochastic. If both seller types are more efficient, then the buyer uses both forms of information; at the early stages of the interaction, the buyer offers high prices that incentivize trade and quality experimentation, and after gathering enough information, the buyer may advance to offering low prices that partially separate seller types. We identify the effect strategic sellers have on the buyer’s optimal strategy relative to more traditional learning dynamics and establish that, paradoxically, when sellers are strategic, the ability to observe delivered quality is not always beneficial for the buyer. This paper was accepted by Victor Martínez-de-Albéniz, operations management.

Optimizing the workload of production units of a make-to-order manufacturing system

This paper proposes a production planning approach for a job shop type manufacturing company in electronics sector that operates with make-to-order (MTO) convention and has a high-mix production range. A novel Mixed Integer Linear Programming (MILP) model is proposed that finds workload-dependent planning horizon by making order acceptance decisions. Partial acceptance of orders and delayed internal target deliveries contribute to the novelty of the proposed model. We establish the complexity of the problem and solve real-life problem instances of a medium-size manufacturing company in the south of The Netherlands. Our computational results show that the proposed MILP model can allocate desired workload to work centers by dynamically adjusting the time horizon for production plans. Our approach can practically be used within reasonable times for daily updates of internal production targets. We perform a sensitivity analysis to show how the time horizon is determined with varying system conditions. The obtained workload amounts satisfy minimum requirements of production units, as this cannot be achieved by the current practice.

The association between moral identity and moral decisions in adolescents.

The present study explores moral identity, moral emotion attribution, moral responsibility, acceptability, and moral decisions in adolescents. We hypothesized that moral identity predicts moral decisions in adolescents. Furthermore, we hypothesized that moral emotions mediate the relationship between moral identity and moral decisions and moral identity and moral responsibility/acceptability. A total of 749 adolescents were recruited. Questions regarding moral identity, moral emotion attribution, moral responsibility, acceptability, and moral decisions were answered by the participants. SPSS and AMOS wereused to analyze the data obtained.The results showed that moral identity was related positively to moral decisions and negatively to moral emotion attribution in adolescents. Mediation analysis showed that moral emotions were a significant mediator between moral identity and moral decisions. The findings indicate that moral identity plays an essential role in the moral decision-making processes in adolescents.

Constitutional and judicial argumentation and the transformation models of normative legal regulation

This article examines the correlation between constitutional argumentation and the models of transformation of legal rules occurring under the influence of constitutional judgments. With references to the case law of the Russian Constitutional Court, the German Constitutional Court, and the United States Supreme Court, the author analyzes argumentative patterns that arise when dealing with specific options for legislative correction. This article focuses on such measures as striking down a legal provision, formulating an exception to a general rule, correcting the hypothesis, disposition, and sanction of a legal norm, clarifying the mechanisms of legal regulation in terms of time, territory, and persons affected, filling legislative gaps and providing additional safeguards. The article makes an original contribution to scholarship because it explores constitutional argumentation instrumentally. Having shown that argumentation serves a supplementary function to judicial review, the author identifies the argumentative patterns prevailing in constitutional discourse and analyzes the mechanism of constructing arguments. It is argued that constitutional reasoning reflects the critical assessment of lex lata and portrays its constitutional deficiencies with a simultaneous projection of normative models that meet constitutional requirements. The article concludes that constitutional argumentation aims to demonstrate constitutionally acceptable legislative decisions and explain why, how, and in what part a challenged law is to be amended.

A comparative and comprehensive study of prediction of Parkinson’s disease

Objectives: Parkinson's Disease (PD) is a form of neurodegenerative disease that is caused the progressive weakening of dopaminergic nerve cells that affects a large number of people around the world. The event of recent treatment methods principally depends upon the experimental data resulting from assessment balances and patients’ journals that take varied boundaries with reference to legitimacy, inter-rater inconsistency, and incessant monitoring. Methods: Nowadays various techniques and algorithms are utilized in predicting the accuracy in PD. A range of those techniques, including SVM, Artificial Neural Network, Naive Bayes, Kernel based extreme learning through subtractive clustering landscapes, Random Forest, The Multi-Layer Perceptron with Back-Propagation Learning Algorithm are widely applied to form the acceptable decision accurately. During this work, and in-depth review was administered on various techniques proposed by numerous researchers. a replacement system must be proposed which uses DL techniques and considers other attributes of paralysis agitans which can improve the prediction and be an advancement within the medical field. Result: It has been observed that many researches have been done in identifying the PD yet there is a need of suitable method or algorithm to improve the prediction of PD which will help in the clinical management. Conclusion and Future work: Most of the methods have used speech as a major attribute for their research and have produced substantial accuracy. In order to increase the precision approaches involving movements, facial expression and other attributes also be considered for evaluation

M-Government experience: a qualitative study in India

PurposeThe purpose of this study is to understand and interpret the fundamental meanings attached to consumer behavior through m-Government (m-Gov) experience and to derive insights. The study explores the extent to which the emergence of m-Gov is able to change citizens established behavior of accessing government services primarily focusing on experience as an important driver.Design/methodology/approachThe study used qualitative methods, and data were collected using semi-structured interviews of m-Gov users. Participants were identified through purposive sampling. Themes are identified from the collected data using thematic analysis.FindingsThe study reveals that m-Gov experience is highly satisfying since it meets citizens' expectations. The findings highlight that in many aspects, m-Gov services provide improved experiences compared to the traditional government and conventional e-Government (e-Gov) services (accessing public services from computers and laptop). The study also reveals a few negative experiences with m-Gov services, like limitations in the use of mobile devices due to their small screen size and high memory consumption, etc.Research limitations/implicationsThe qualitative method used in this study focuses clearly on unearthing depth and richness of understanding rather than the breadth of generalization. The sample size is apt for drawing reasonable conclusions. Scholars and information systems researchers may use some insights for future research.Practical implicationsm-Gov services in a developing economy like India is a new and emerging area for managers. The findings about the elements of m-Gov experience and how the experience influences citizens' acceptance decisions will benefit managers and policymakers in implementing m-Gov services or projects.Originality/valueThis study is one of the early attempts to explore the m-Gov experience. The research is important as experience affects users' attitude, a precursor to behavioral intention, which in turn, influences their behavior.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-10-2020-0482

A model of the editorial process in academic journals

Editors of academic journals make their acceptance or rejection decisions about submitted papers based on their own prior assessment of the intrinsic quality of these papers augmented by the information in reviewer recommendations. In this paper, we theoretically analyze the editorial process of academic journals, and in particular, the editors’ extraction of information about intrinsic paper quality from reviewers. We assume that, if a reviewer’s own research is close to the research area of a paper, he is likely to have greater expertise in evaluating that paper (i.e., be an “expert” reviewer) but is also more likely to be positively or negatively biased with respect to it. On the other hand, “generalist” reviewers, whose own research is further away from the research area of the paper, are likely to be unbiased about it; however, their expertise in evaluating the paper is likely to be lower as well. We further argue that the editorial decisions of journals will deviate considerably from the socially optimal rule of accepting good papers and rejecting bad papers if the above potential reviewer biases are not taken into account by editors when choosing reviewers. We show that editors can make better editorial decisions if they choose the appropriate type of reviewer to evaluate a paper (in the one reviewer case) or the appropriate combination of reviewer types (in the two reviewer case), based on their own prior assessment of submitted papers. We also show that, if the editor can aggregate the information contained in multiple reviews efficiently, two reviewers are better than one as long as the cost of using an additional reviewer is moderate; however, two reviewers may be worse than one if the editor adopts ad hoc decision making rules such as requiring both reviewers to recommend acceptance of a paper before the journal can accept it.

An integrated production and transportation scheduling problem with order acceptance and resource allocation decisions

In the global market competition, coordinating decisions and integrating different aspects of supply chains would help managers promote the overall performance of systems, and reduce the overall costs. The integration of production and transportation is one of the essential considerations of manufacturers to achieve more efficiency levels. In this paper, an integrated production and transportation scheduling problem is addressed. The objective is to maximize the revenue of accepted orders and to minimize the sum of delivery cost, weighted tardiness cost, and fixed cost of vehicles, by considering resource allocation and resource-dependent processing times. The problem is regarded as strong NP-Hard. A Mixed Integer Linear Programming (MILP) is proposed. Also, two meta-heuristic solution approaches, based on Adaptive Genetic Algorithm (AGA) and Tabu Search (TS) are proposed to solve this problem. The computational experiments based on small-scale and large-scale analyses are provided to assess the effects of different parameters of the problems on both optimality and solving time criteria. It is deduced that the proposed AGA has a better performance and higher efficiency, rather than the proposed TS, in terms of optimality. The results indicate the capability of the proposed algorithms in solving the real-life problems in an efficient manner.

Preface

International Conference of Emerging Challenges in Engineering and Current Technology (ICECT III) 30-31 March 2021ICECT III 2021 is organised by Universiti Teknologi MARA, Terengganu Branch to provide an opportunity to research scholars, delegates, academicians, and students to engage in dialogues on a wide range of topics in engineering challenges and current technology. Formerly launched in 2016 as the International Symposium of Engineering Technologies (ISET), the conference name was then changed to the International Conference on Engineering and Computing Technology (ICECT II) in 2018. Now the conference is rebranded as the International Conference of Emerging Challenges in Engineering and Current Technology (ICECT III) to ensure it is relevant to the latest emerging challenges in engineering and current technology. The main aims of ICECT III 2021 are to promote the emerging development of the current technology in the engineering field to encourage engineering applications on the current state-of-the-art in the various engineering fields. The conference provides a mechanism to communicate findings, solutions or innovations among scholars and scientists as well as motivate scholars, scientists, and engineers in related fields to provide to its body of knowledge. The conference focused on original research work in a multiple range of areas, including Chemistry and Chemical Engineering, Electrical and Electronics Engineering, Materials and Mechanical Engineering, Advanced Computing Technology, Applied Sciences and Mathematics. Also, it serves as a conduit for channelling advanced engineering research and applications to the regional industry. All submissions are subjected to rigorous review before an acceptance decision is made. The papers published in these proceedings will contribute to a better understanding of the related problem, the current solution and the future innovation as well as will serve as a reference for researchers and engineers in the field of electrical, mechanical, chemical, advanced computing technology and applied mathematics. The online live streaming presentation is used as a method of presentation within the two-day conference.List of Organizing Committee of Conference, Editors of ICECT III 2021 are available in this pdf.

Multi-scale covering rough sets with applications to data classification

When facing with a complex problem, one often needs to consider dealing with it at what level of granularity. Multi-scale knowledge representation provides us an opportunity to analyze problems from different granularity. However, as well as traditional rough sets model, most of existing multi-scale rough set models are based on partitions generated from equivalence relations, which limits their application in real data. In this paper, we set forth a new data analysis model with multi-scale coverings by extending partitions to coverings. To this end, a new type of decision tables, i.e., multi-scale covering decision tables are formalized to deal with knowledge representation under multi-scale framework. Optimal scale selection for consistent and inconsistent covering decision tables are then proposed to obtain acceptable decisions under coarser scales. Furthermore, the acquisition of optimal rules with higher accuracy and covering rate are discussed. Extensive experiments on some real-world data sets are set up to examine the effectiveness and feasibility of the proposed model. Experimental results show that the multi-scale covering theory gives a new way to enhance the generalization ability of the classification model.

Scholarship Acceptance Decision Support System at SMKN 4 Bengkulu City using the AHP method

The objectives to be achieved in this research are: 1). Creating a Decision Support System (SPK) in receiving scholarships using the Analytical Hierarchy Process (AHP) method 2). The SPK made to determine scholarship recipients is in the form of providing assistance free of committee fees. The research method used in this research is from a conventional system to a computer system in the form of an Analytical Hierarchy Process method. The software used in making a Decision Support System (DSS) in receiving scholarships uses the Analytical Hierarchy Process method using the PHP programming language and mySQL database. The criteria in the scholarship recipient data through the Analytical Hierarchy Process method consist of report cards, academic scores and non-academic scores. The results of the Decision Support System are in the form of ranking students (students) based on the calculation of criteria and student alternatives that are calculated using the Analytical Hierarchy Process method.

A matheuristic for making order acceptance decisions in multi-product, multi-stage manufacturing systems

We discuss a planning model with load-dependent lead times for making order acceptance decisions in multi-product, multi-stage manufacturing systems. Semiconductor wafer fabrication facilities (wafer fabs) belong to this class of manufacturing systems. A profit-based objective function is considered. Clearing functions are used in the planning formulation to correctly represent the lead time behavior in the case of a congested system. Order acceptance decisions are made with respect to flexible due dates, i.e., it is possible to reject certain orders if there is not enough capacity. Such acceptance decisions are important, for instance, in short-term demand supply matching algorithms that are crucial for demand fulfillment and available to promise decisions in semiconductor manufacturing. The resulting planning problem is formulated as a mixed integer linear program. We first show that the resulting planning problem is NP-hard. Hence, computationally tractable approaches must be designed. Therefore, variable neighborhood search is hybridized with linear programming to solve large-sized problem instances in reasonable amount of computing time in the present paper. Results of computational experiments for problem instances that are derived from a scaled-down wafer fab simulation model are provided and analyzed. The computational results demonstrate that the proposed matheuristic outperforms time-based decomposition approaches from the literature.

Does office size matter in client acceptance decisions? Evidence from big 4 accounting firms

Does office size matter in client acceptance decisions? Evidence from big 4 accounting firms

To cross or not to cross? Review and meta-analysis of pedestrian gap acceptance decisions at midblock street crossings

IntroductionPedestrians are vulnerable road users exposed at risk during their interaction with vehicles in uncontrolled urban areas. In this paper, a critical overview of the literature and meta-analyses were conducted on the topic of pedestrian gap acceptance choice during midblock street crossings. MethodΑ large number of studies examining personal and contextual factors affecting midblock crossing choices were identified. In an effort to condense research outputs, a quantitative approach was adopted alongside qualitative assessments. Meta-analyses were conducted to summarize the impacts of various predictor variables on pedestrian gap acceptance probabilities from binary logistic models in midblock locations. After application of a rigorous set of criteria, 14 publications were considered appropriate to conduct meta-analyses on beta coefficients of four factors: vehicle speed (VS), gap size (GS), waiting time (WT) and frequency of attempts (FA). ResultsStatistically significant results were obtained for VS, GS and FA. Specifically, it was determined that for one unit increase in incoming VS, there is a 10% decrease (OR=0.9) in the odds of pedestrians crossing the road (accepting the incoming time gap) for pedestrians. For one unit increase in temporal GS, the odds of crossing the road become 7.22 times larger for pedestrians. For each crossing attempt, as measured by FA, the odds of crossing the road become 16.6 times larger for pedestrians. WT was determined to have a non-significant impact on pedestrian crossing odds. ConclusionsTo the best of the authors' knowledge, this is the first time that a meta-analysis of the critical factors influencing gap acceptance of pedestrians in midblock street crossings is carried out. Study findings are useful for practitioners and policymakers to formulate appropriate management plans to reduce interactions of pedestrians and vehicles at uncontrolled urban midblock locations. Future research is needed to further understand the determinants of gap choice decision making.

Promoting Patient Engagement in Medical Informed Consent – A Qualitative Study of Chinese Doctors’ Communication Strategies

ABSTRACT Patient engagement is now widely endorsed as an essential ingredient for high-quality healthcare, yet there has been limited research on how patient engagement can be facilitated in medical informed consent (IC) communication. To address this gap, a fine-grained discourse analysis was conducted to identify communication strategies adopted by doctors to facilitate information delivery and ascertain patients’ understanding, which translate into an increase in patient engagement. Data was collected from a public hospital in mainland China. Nonparticipating observations of 14 IC sessions were audio-recorded, followed by in-depth, semi-structured interviews with those observed patients. Four communication strategies emerged from the analysis: 1) seeking patients’ understanding of their condition; 2) explaining medical information by reference to shared knowledge and practice; 3) recognizing and addressing patients’ psychological concerns; 4) repeating critical information and checking patients’ understanding through teach-back. The adoption of these strategies enables doctors to tailor the scope and delivery of information to accommodate and address patients’ preferences, rather than defaulting to one-way information dumping. This study sheds light on the complexity of IC and further contributes to the ongoing endeavors to improve IC communication by raising the awareness of the role of patients in making mutually acceptable decisions. These identified strategies can be incorporated into medical communication training to facilitate delivery of healthcare that is sensitive to patients’ needs and expectations.

Evaluating Risk in an Abnormal World: How Arbitrary Probability Distributions Affect False Accept and Reject Evaluation

Risk mitigation strategies commonly use the test uncertainty ratio (TUR) and end-of-period reliability (EOPR) to ensure a measurement is adequate for making acceptance decisions. Unfortunately, the common guidance of maintaining a TUR of at least 4:1 was developed to simplify the underlying calculus in an era predating modern computing and assumes that the probability distributions describing the product and the measurement uncertainty are unbiased and normally distributed. The Guide to the Expression of Uncertainty in Measurement and its supplements describe several situations where uncertainty in the measurement will not follow a normal distribution. Despite the evidence of non-normal behavior in measurements and products, risk evaluations typically assume normality in both distributions. While evaluating the probability of false accept (PFA) and the probability of false reject (PFR) is more challenging when the probability distributions are non-normal, the calculus is straightforward using either numerical integration or Monte Carlo techniques. This work considers several case studies of risk evaluation, including both global and specific risk, when the product or the test measurement uncertainty do not follow normal distributions. Neglecting non-normal behavior might greatly affect PFA and PFR by either over- or underestimating the probabilities depending on the parameters of the distributions.

Employee Acceptance Decision Support System Using The Smart Method (Case Study on Association of The Indonesian Electrical and Mechanical Contractors Samarinda City)

Decision Support System (DSS) is a system that can assist someone in making accurate and targeted decisions. Many problems can be aasolved by using SPK, one of which is the acceptance of employees at the Gaklimdo Samarinda Association using the SMART (Simple Multi Attribute Rating Technique) method. The purpose of this research is to produce a Decision Support System for Employee Admission Using the SMART Method (Case Study of the Gaklimdo Association of Samarinda City) with the hope that the selection of employees will be carried out objectively. By using the PHP programming language and the database used is MySql. In this study, the data collection techniques used were literature study, observation and interviews. The result of this research is the creation of a decision support system to make employee decisions that are accepted, and not accepted as employees. Users can input prospective employee data, criteria data and sub-criteria data. Then the system will look for a solution using the SMART (Simple Multi Attribute Rating Technique) method. After the decision is obtained, the system will display the decision.

UI Generosity and Job Acceptance: Effects of the 2020 CARES Act

To provide economic relief following the onset of the COVID-19 pandemic, the U.S. CARES Act granted an extra $600 per week in unemployment insurance (UI) benefit payments from late March through July 2020. This unprecedented increase in UI generosity caused weekly benefit payments to exceed prior earnings for most recipients, raising concern that many would be unwilling to accept job offers, slowing the labor market recovery. To assess the impact of the UI supplement, we analyze the job acceptance decision in a dynamic framework in which job seekers weigh the value of a job against remaining unemployed, accounting for the perceived state of the labor market and expected weeks of UI benefits. We derive a reservation level of benefit payments at which an individual is indifferent between accepting and refusing a job offer at their prior wage. Calculating the reservation benefit and comparing it to imputed benefit payments for a wide range of U.S. workers suggests that only a small fraction would turn down an offer to return to work at their previous wage under the CARES Act expanded UI payments. We supplement this quantitative assessment of reservation benefits with direct empirical analysis of labor force transitions using matched Current Population Survey (CPS) data, linked to annual earning records from the CPS income supplement to form UI replacement rates. The results show moderate disincentive effects of the $600 supplemental payments on job finding rates and by extension small effects of the $300 weekly supplement available during 2021.

Prediction of COVID-19 cases during Tokyo's Olympic and Paralympic Games.

Tokyo's Olympic and Paralympic Games set to begin in late July 2021 without spectators from abroad, but vaccine rollout has been slow in Japan compared with other developed countries. In this study, COVID‐19 epidemic curve in Tokyo is developed based on weekly reported data from January 23, 2020, until April 16, 2021. The maximum daily number of infected cases in Tokyo in August 2021 would be 7,991 if the current pace of vaccinations would be used (1/1,000 per day). This daily number is greater than the highest daily cases (2,447) recorded on January 7, 2021. However, if the rollout pace could be doubled (1/500 per day), the peak daily new cases would be 4,470 in August. If it could be quadrupled (1/250 per day), the peak would be noted at 2,128 in July and the highest number in August would be 1,977. If vaccine rollout could not be enhanced, the cancellation might be an acceptable decision, since health is the most precious to local people and our Olympians.