Business-to-business selling in the post-COVID-19 era: Developing an adaptive sales force

The COVID-19 pandemic has changed how salespeople interact with customers and with business-to-business (B2B) organizations. Organizations must confront the shifts in how their salespeople operate. Recent research recommends firms develop an adaptive sales force to address disruptions like a pandemic and be prepared to meet such challenges in the future. Based on interviews with marketing and sales executives, we explore how firms have responded to these interconnected changes during the COVID-19 pandemic and offer insight into best practices deployed across industries.     

Enhancing adaptive transfer of cross-cultural training: Lessons learned from the broader training literature

Drawing insights from the broader training literature, we argue that evaluation of cross-cultural training effectiveness should adopt comprehensive criteria, including cognitive, skill-based, and affective learning outcomes as well as adaptive transfer. We propose that the integration of an error management supplement in cross-cultural training can enhance trainee acquisition of self-regulation skills and self-efficacy that facilitate adaptive application of learning to novel cultural situations. In addition to the traditional error management training designs (i.e., positive error framing), the current paper describes additional design elements to promote acquisition of cognitive strategies, prevent premature automaticity, alleviate concerns about error occurrence during learning, and enhance readiness to transfer. In addition, we offer propositions regarding the effects of the supplement on learning and transfer outcomes, along with implications for future research and practice on cross-cultural training.     

Influence of livelihoods on climate change adaptation for smallholder farmers in the Mekong Delta Vietnam

Farmers in the Vietnamese Mekong Delta face a wide range of climate-related and hydrological factors which threaten rice production. Smallholder farmers must adapt to climate change to sustain rice production as their central and most important livelihood activity. A sample was stratified across agro-ecological areas in the Delta affected by flooding, alluvial soils, acid sulphate soils, and saline water intrusion and by derived farmer typologies. A rural livelihoods approach was used in focus group discussions and in-depth interviews to identify and enumerate enabling and constraining adaptation factors. Smallholders experienced diverse natural hazards such as floods, abnormal rains, high temperatures, water scarcity, and salinity intrusion specific to the agro-ecological areas. Adaptation was constrained by labour shortages, water quality, topography, access to combine harvesters, transportation infrastructure, dryers and household savings. Adaptation was enabled by farming techniques and experience, cooperative groups, water quantity, access to information, and ability to purchase agro-chemicals through credit. Small farmers (< 1?ha) were more constrained than large farmers (> 1?ha) who had an expanded livelihood asset base. A range of policy implications are discussed, but adaptation is not just about technological fixes but requires overall improvements in a range of human, social and financial components.     

Unemployment and econometric learning

We apply well-known results of the econometric learning literature to the Mortensen and Pissarides real business cycle model. Agents can always learn the unique rational expectations equilibrium (REE), for all possible well-defined sets of parameter values, by using the minimum-state-variable solution to the model and decreasing gain learning. From this perspective the assumption of rational expectations in the model could be seen as reasonable. But using a parametrisation with UK data, simulations show that the speed of convergence to the REE is slow. This type of learning dampens the cyclical response of unemployment to small structural shocks.     

Data-based mechanistic modelling and forecasting globally averaged surface temperature

The main objective of this paper it to model the dynamic relationship between global averaged measures of Total Radiative Forcing (RTF) and surface temperature, measured by the Global Temperature Anomaly (GTA), and then use this model to forecast the GTA. The analysis utilizes the Data-Based Mechanistic (DBM) approach to the modelling and forecasting where, in this application, the unobserved component model includes a novel hybrid Box-Jenkins stochastic model in which the relationship between RTF and GTA is based on a continuous time transfer function (differential equation) model. This model then provides the basis for short term, inter-annual to decadal, forecasting of the GTA, using a transfer function form of the Kalman Filter, which produces a good prediction of the ‘pause’ or ‘levelling’ in the temperature rise over the period 2000 to 2011. This derives in part from the effects of a quasi-periodic component that is modelled and forecast by a Dynamic Harmonic Regression (DHR) relationship and is shown to be correlated with the Atlantic Multidecadal Oscillation (AMO) index.     

Completion deadlines,adaptive learning assignments,and student performance

The authors examine effects on student performance, from applying different deadline strategies to online assignments in face-to-face principles of macroeconomics courses. Students in one section were required to complete weekly adaptive learning assignments. Students in the second section were required to complete the adaptive assignments only before each of the three exams administered in the course, thereby giving students more control over when to complete them. The findings show that the presence of rigid deadlines detracted from student participation with the adaptive learning assignments. However, the assignments contributed positively to performance in the traditional assessment categories of homework and exams.     

“双创”时代高校创客空间的复杂适应系统分析及优化策略

“大众创业、万众创新”时代，创客空间建设已成为高校扶持大学生创业的主流模式和重要举措。基于复杂适应系统（CAS）理论，阐释了高校创客空间的开放性、非线性、自组织性和涌现性等系统特征，指出高校创客空间的演进过程包括接触和交互两个阶段，在其演进过程中，标识机制和积木机制扮演着重要角色。最后，从CAS视角提出促进高校创客空间发展和系统优化的建议。     

The construction of a composite index for general satisfaction in Turkey and the investigation of its determinants

The goal of public enterprises is to increase the wealth and happiness of individuals. This target lets them make the investment to fulfill these expectations. However, not all citizens in a country might have the same opportunities. This case requires comparing citizens who live in cities with regard to satisfaction levels. This study consists of two stages. In the first stage, a composite index called the General Satisfaction Index (GSI), which aims to explain the satisfaction levels of citizens living in cities, is obtained by using the Benefit of the Doubt method. Contrary to the previous studies, the satisfaction indicators based on the emotions and senses of citizens are used to construct a composite index. Satisfaction indicators’ contribution to GSI is ensured using the minimum weights. In the second stage of this study, socioeconomic determinants of GSI are investigated in both countrywide and regions by using regularized regression methods. In the result of the regression analyze, it is confirmed that there are different socioeconomic determinants of satisfaction levels for countrywide and each region. For this reason, it is proposed that policymakers should follow different politicizes in each region to increase the satisfaction levels of citizens in the country.     

重大突发公共卫生事件科技研发应急体系运行机制研究

在突发事件应急管理过程中，科技研发支撑作用十分关键，亟需建立突发公共卫生事件科研应急体系长效运行机制。基于科技研发应急体系的复杂性、适应性特征，从主体属性和体系架构两个层面入手，分析重大突发公共卫生事件科技研发应急体系的复杂适应系统特征，运用复杂适应系统理论模型和动态闭环螺旋模型，研究重大突发公共卫生事件科技研发应急体系运行机制。研究发现，重大突发公共卫生事件背景下，科技研发应急体系具有复杂适应系统的7个基本特征，是典型的复杂适应系统；科技研发应急体系中，科研攻关专家组、诊疗医院、科技部门等科研主体具有各自内部模型，并通过6大标识进行聚集；科技研发应急体系存在事件—需求引导、主体协同、资源交互、成果转化机制。同时，新冠病毒肺炎疫情应对实践表明，疫情防控救治进程中科技研发应急体系存在并遵循上述运行机制。     

Performance Measures in Dose-Finding Experiments

In the first phase of pharmaceutical development, and assuming that the probability of positive response increases with dose, the main statistical goal is to estimate a percentile of the dose–response function for a given target value $\mathrm{\Gamma }$. We compare the Maximum Likelihood and centred isotonic regression estimators of the target dose and we discuss several performance criteria to assess inferential precision, the amount of toxicity exposure and the trade-off between them for a set of some exemplary adaptive designs. We compare these designs using graphical tools. Several scenarios are considered using simulation, including the use of several start-up rules, the change of slope of the dose-toxicity function at the target dose and also different theoretical models, as logistic, normal or skew-normal distribution functions.