Bridging the gaps in decision making under uncertaintyThis special issue (SI) of Human Systems Management includes papers in the topic "Bridging the gaps in decision making under uncertainty", in collaboration with the 51 st Annual Conference of Southeast Decision Science Institute (SEDSI).We have witnessed an unprecedented level of uncertainty in recent times and decision making under uncertainty is always a challenge.The COVID-19 pandemic exposed a series of gaps in decision making in uncertain environment leading to disastrous consequences for businesses, communities, and in our own lives.This SI called for further research in theories and applications to deal with uncertainty and mitigate risks from managerial and organizational perspectives.The topics of the SI are of particular interest in the context of the pandemic environment as there have been ongoing efforts to refine and redefine decision making in business and society through the lens of equity, inclusion, and sustainability.The submissions from researchers across the globe covered a wide range of fields of research and applications impacting business and society as a whole.After thorough peer-review, six manuscripts covering different topics, yet broadly connected to the theme of the SI, are included in this issue.Brief descriptions of these six articles are provided below.Low and Memon investigate how micro-level CSR practices impact two categories of engagement: job engagement and organizational engagement in Malaysia during early lockdown periods due to the COVID-19 pandemic.They develop a conceptual model using Stakeholder Theory, Social Exchange Theory, and Engagement Theory and test it by analyzing data collected using partial least squares structural equation modeling (PLS-SEM).Employees' involve-

After the sudden lockdown in Wuhan on January 23, 2020, various nonpharmaceutical interventions (NPIs) were mandated in China to stop the spread of COVID-19.1 Studies indicated that mandatory NPIs policy was effective in limiting the spread of COVID-19.2 On one hand, mandatory NPIs are argued to promote public health by building upon the social benefit of practicing NPIs1 and, on the other hand, mandatory NPIs have been argued to restrict the bodily freedom while increasing paternalistic control of the state.3 Generally, health authorities have tended to suggest voluntary NPIs policy to appeal to individuals.4 This study aims to examine Chinese public’s attitudes toward mandatory and voluntary NPIs and their adoptions at the initial stages of COVID-19. The current study contributes to the public health literature by investigating possible policy influences on NPI practices during a pandemic. Based on the findings, the current study identified a set of recommendations to address protective health behaviors during a pandemic, which can be used by governments and public health practitioners to prepare and respond to a pandemic.

Introduction Job insecurity such as loss of jobs or reduced wages has become a serious social problem in the US since COVID-19 started. Combined with psychological distress and experience of COVID-19 symptoms, the changes of people’s protective behaviors vary across states in the US. Methods This research investigated racial differences in the COVID-19 related factors among White, Black, and other minorities in the US, and examined how mental health mediated the impact of job insecurity on protective behaviors, and how the COVID-19 symptoms moderated the mediation effect of mental health. The 731 valid responses in a cross-sectional survey from May 23 to 27, 2020, in the US were analyzed with independent sample t-tests, Pearson’s chi-square tests, and path analysis. Results The findings showed that there were significant differences in job insecurity and Nonpharmaceutical Interventions (NPIs) practice among White, Black, and other minorities. Job insecurity was significantly negatively associated with NPIs practice and was significantly positively associated with mental health. Mental health significantly partially mediated the effect of job insecurity on NPIs practice, in that job insecurity is a better predictor of NPIs practice for individuals with worse mental health than that for individuals with better mental health. Experience of COVID-19 symptoms moderates the mediation effect of mental health on the relationship between job insecurity and NPIs practice, in that mental health is a better predictor of NPIs practice for individuals with a higher experience of COVID-19 symptoms than for individuals with a lower experience of COVID-19 symptoms. Discussion The findings in this study shed lights on psychological and behavioral studies of people’s behavior changes during a pandemic. The study indicates the importance of treating mental health to promote protective behaviors during a pandemic, as well as advocating for employees by identifying the needs for those whose jobs were negatively impacted the most.

Measuring Green Technology R&D Efficiency (GTR&DE) and identifying improvement potentials are of paramount importance for governmental policymaking on sustainable technology R&D investment. However, comprehensive evaluation of GTR&DE and integration with projection analysis are largely lacking. To support government policymaking, we propose a new DEA-SBM-PA approach integrating Data Envelopment Analysis Slack Based Measurement (DEA-SBM) and projection analysis (PA) to identify how well sustainable R&D innovations are performing and how much improvements are needed. Based on the theory of decoupling, this study constructs a systemic framework of GTR&DE including economic, energy, and environmental performance indicators. Subsequently, the GTR&DE scores at provincial, regional, and national levels are measured by DEA-SBM model based on panel data from China's 30 provinces during 2011-2017, and the potential GTR&DE improvements for the provinces deemed inefficient are assessed by projection analysis. Our findings reveal that implementation of green technology R&D innovation has positive impact in achieving comprehensive benefits covering economy, energy, and environment. Furthermore, the major contribution of this research is to develop a unifying framework to provide insights for policymakers, including assessment of GTR&DE at provincial, regional, and national levels, analysis of both spatial and temporal differences of GTR&DE scores at different levels, identification of efficient and inefficient provinces, and finally projecting GTR&DE improvement potentials for the provinces deemed inefficient. The findings have significant policy implications, particularly because they demonstrate the impact of an important government policy adjustment in 2015 by analyzing both before and after effects. Finally, it is discussed how government initiatives for sustainable technology R&D innovation may be supported with additional analysis in future.

Due to the increasing amount of new information that is emerging about COVID-19, traditional and web-based information sources are commonly used to spread and seek information. This study compared differences in information seeking, trust of information sources, and use of protective behaviors (e.g., mask wearing) among individuals in the US and China during the COVID-19 pandemic. A total of 722 valid responses in the US and 493 valid responses in China were collected via online surveys in May 2020. Pearson's Chi-square tests, independent samples t-tests, and multiple linear regressions were used to conduct the analyses. Results showed that US respondents accessed significantly fewer COVID-19 information sources, rated significantly lower levels of trust in these sources, and reported significantly lower levels of protective behaviors than the Chinese respondents. In both countries, trust in newspapers, radio/community broadcasting, and news portals were significantly positively correlated with protective behaviors. While trust of TV was significant in both populations, in China it was positively correlated, whereas in the US was negatively correlated, with protective behaviors. Findings from this study showed that coordinated and consistent messages from governmental officials, health authorities, and media platforms are important to promote and encourage protective behaviors.

Aiming at the unique characteristics of agro-food supply chain, this study builds a comprehensive model to investigate the impact of agro-food supply chain integration, composed of internal, supplier and customer integration, on agro-food product quality and financial performance. It explores the relationships among these factors using the data from 162 Chinese agro-food processing businesses. The findings reveal that internal integration and supplier integration are the critical factors to improve product quality within the context of agrofood supply chain. Moreover, the product quality fully mediates the relationship between internal integration and financial performance, and the relationship between supplier integration and financial performance. This study indicates that securing product quality and food safety is an effective way to achieve better financial performance for agro-food processing businesses. This study could help agro-food processing businesses understand the value creation roles of agro-food supply chain integration and provide valuable guidance for them to decide how to respond to various challenges and manage agro-food supply chain integration in order to improve product quality and achieve higher financial performance. A unique contribution of this study is to provide a theoretical framework for advancing the agro-food supply chain integration literature, which thereby could be expected to open a starting point for more agro-food supply chain integration research in the future.

The rapid growth of analytics is bringing more attention to quantitative core curriculum requirements in undergraduate business programs. Statistical knowledge and skills are unequivocally recognized as essential cornerstone of business analytics. Furthermore, educational research has shown that academic performance in statistics classes is related to the attitudes that students bring to the course. This article assesses the reliability and validity of the Survey of Attitudes toward Statistics (SATS) in measuring noncognitive dimensions of attitudes among undergraduate business students. Sample data from U.S. and Chinese introductory business statistics classes were collected and analyzed to learn more about this aspect of student engagement across business schools located in countries with substantially different levels of success in international mathematics achievement testing, as well as differing cultural and educational practices. Results show that the six-factor model structure of the SATS provides a good fit in both populations, with students entering business statistics holding only slightly positive attitudes toward the subject. Significant distinctions between four of the six attitude components were identified. Implications of measuring and improving these attitudes are discussed. Business statistics instructors are encouraged to use the survey as a standardized instrument to measure effects of interventions and make evidence-based pedagogical decisions.

In International Engineering, Procurement, and Construction (IEPC) projects the main contractor carries out the work at distant sites for the project owner with support from multiple suppliers and/or subcontractors. Managing relationships with suppliers and/or subcontractors in such projects is even more critical due to additional dependency on them to complete the job. Yet it is not clear which factors influence such relationships in IEPC projects. This study intends to close this gap in the extant literature. Data has been collected from professionals involved in IEPC projects and it has been investigated how various aspects of relationship with suppliers and/or subcontractors may influence project outcomes. Logistic regression and neural networks have been used to analyze the data and subsequently identify four critical factors: service provided by suppliers and/or subcontractors, continuous improvement, supplier and/or subcontractor delivery reliability, and effective problem solving, which impact IEPC project success to the greatest extent. The findings suggest that the main contractors should pay particular attention to these aspects of relationship management.

Predicting criminal recidivism effectively is of major interest in criminology. In this paper, we study the ability of the support vector machines (SVM) to predict the probability of reincarceration. As a semi parametric approach, the SVM minimizes structural risk whereas nonparametric models, such as neural networks, minimize empirical risk. Furthermore, the SVM differs significantly from existing parametric models, such as logistic regression, in prediction of criminal recidivism. Due to the relatively new application of the SVM in predicting criminal recidivism in the field of criminology, a general framework is presented for how the SVM may become a supplemental or alternative method for recidivism prediction. Comparisons among logistic regression, neural networks, and the SVM are made with empirical testing results on a well-known recidivism data set. A combined prediction utilizing all three methods provides the most flexibility and accuracy in decision-making.

This paper presents a differential-algebraic approach for solving linear programming problems. The paper shows that the differential-algebraic approach is guaranteed to generate optimal solutions to linear programming problems with a superexponential convergence rate. The paper also shows that the path-following interior-point methods for solving linear programming problems can be viewed as a special case of the differential-algebraic approach. The results in this paper demonstrate that the proposed approach provides a promising alternative for solving linear programming problems.

Prediction of criminal recidivism has been extensively studied in criminology with a variety of statistical models. This article proposes the use of neural network (NN) models to address the problem of splitting the population into two groups — non-recidivists and eventual recidivists — based on a set of predictor variables. The results from an empirical study of the classification capabilities of NN on a well-known recidivism data set are presented and discussed in comparison with logistic regression. Analysis indicates that NN models are competitive with, and may offer some advantages over, traditional statistical models in this domain.

An important aspect of direct marketing research focuses on developing and segmenting a house list (customer database) using various geographic, socioeconomic, and recency, frequency and monetary (RFM) measures. For a typical promotion, direct marketers may take a simple random sample from the house list as a test mailing to forecast the segment rollout response rates. Decisions about the final rollout are made in such a way that only the segments with response rates over the prespecified threshold or break-even response rate will be used. In this article, it is shown that the commonly used simple random sampling procedure may seriously underestimate the variability of the rollout response rates of segments with higher test response rates, overforecast the potential number of buyers from the rollout, and inflate forecast accuracy. Several procedures are proposed to improve the house list tests, and examples are used to compare the new procedures with the existing one. Results show that the proposed house list test procedures provide more statistically efficient, cost-effective, and reliable forecasts for segment response rates while improving the accuracy of forecasts generated.

A generalized unified mathematical model of neural learning is proposed. A learning potential function is defined. A broad class of problems which are related to neural learning are examined. Differential inclusions for finding the minimum of the learning potential functions are derived. The general convergence theorem of optimal solutions are proved and its applications to the supervised learning, unsupervised learning, and Hopfield neural networks are investigated.