Opel Eisenach GMBH—Creating a high-productivity workplace

The German automotive industry continues to struggle against increasingly adverse conditions—pampered workers, inefficient production techniques, and a strong currency that threatens to price cars out of the market. Enter an unlikely competitor-General Motor's Opel subsidiary in Eisenach, in the former German Democratic Republic. Designed as a unique experiment, the recreated plant combines a lean production system, cellular assembly, flexible working arrangements, learning opportunities, and teamwork to create a high-productivity workplace. Interviews with workers at Opel Eisenach show that this approach has a profound impact on worker motivation. The plant produced 160,000 cars in 1995, a remarkable accomplishment that has caught the attention of the entire European auto industry.     

Learning in a multilateral bargaining experiment

This paper analyzes data from an investigation of a majoritarian bargaining experiment. A learning model is proposed to account for the evolution of play in this experiment. It is also suggested that an adjustment must be made to account for the panel structure of the data. Such adjustments have been used in other fields and are known to be important as unadjusted standard errors may be severely biased downward. These results indicate that this adjustment also has an important effect in this application. Furthermore, an efficient estimator that takes into account heterogeneity across players is proposed. A unique learning model to account for the paths of play under two different amendment rules cannot be rejected with the standard estimator with adjusted standard errors, however it can be rejected using the efficient estimator. The data and the estimated learning model suggest that after proposing “fair” divisions, subjects adapt and their proposals change rapidly in the treatment where uneven proposals are almost always accepted. Their beliefs in the estimated learning model are influenced by more than just the most recent outcomes.     

Social Interactions and Entrepreneurial Activity

We show that individuals residing in highly entrepreneurial neighborhoods are more likely to become entrepreneurs and invest more into their own businesses, even though their entrepreneurial profits are lower and their alternative job opportunities more attractive. Our results suggest that peer effects create nonpecuniary benefits from entrepreneurial activity and play an important role in the decision to become an entrepreneur. Alternative explanations, such as entry costs, social learning, and informal credit markets, are not supported by the data.     

创新教学方法,提高生理实验课教学质量

生理实验是生理学教学中比较重要的一个部分,大力提高生理实验教学的质量有利于促进相应的生理教学质量的提高。生理实验教学的过程之中,根据具体的现实状况改进实验教学的有关内容具有重大的意义,因为这样的措施能够在一定程度上提高学生对生理实验课的学习欲望和为学生制造更多的实践动手机会,有助于提高学生基本技能及临床技能水平,培养学生良好的职业道德素质和综合素质。     

Planning knowledge for phased rollout projects

This paper explores how projects that can be considered as repeatable and replicable in a sequence of deliverables over a period of time can be planned and executed (rolled out) in a way that maximizes knowledge transfer and production from one execution to the next. A variety of management disciplines were reviewed to understand the way that project and program rollouts are treated in IT, product development, organizational learning, and manufacturing literature. These reviews enabled the development of a conceptual model to describe how planning and implementing the rollout of a product or service can be more knowledge‐focused and an accompanying capability maturity model. Several examples from real life, including one experienced by one of the authors, were used to illustrate and test the conceptual model. The model integrates knowledge management, change control, and planning in a way that can develop organizational learning and the authors assert that this can improve productivity and both tangible and intangible returns to those that follow the model. An accompanying capability maturity model is also presented. The conceptual model, while initially tested against several real‐life cases, needs to be tested against a new set of projects as an action learning research project in order to more fully develop knowledge of the dynamics of learning and improvement in rollout projects. The implication is that when project managers undervalue important learning aspects of phased rollouts, they may miss important organizational learning opportunities.     

Measuring the quality of managerial learning on the job

This article describes the development of an instrument to measure the quality of managerial learning on the job. The instrument can be used to analyse the quality of the individual learning process on the job. The literature shows that two factors determine the quality of the learning process: the learning potential of the job context and the way in which the manager approaches their work. So the instrument has two components. The first component measures the four types of work experience that offer potential opportunities for individual learning. These are transitions, task-related characteristics, obstacles and support. The second component, the so-called learning behaviour, analyses the way the individual approaches the potential learning opportunities present in the job. This can also be divided into four categories: emergent learning, planned learning, instruction-oriented learning and meaning-oriented learning. Based on these two components, an instrument has been developed to measure the quality of learning on the job. This has been shown to be valid and reliable in a sample of European managers.     

Adapting to environmental jolts

This paper examines organizational adaptations to an environmental jolt--a sudden and unprecedented event (in this case, a doctors' strike)-- that created a natural experiment within a group of hospitals. Although adaptations were diverse and appeared anomalous, they are elucidated by considering the hospitals' antecedent strategies, structures, ideologies, and stockpiles of slack resources. Assessments of the primacy of the antecedents suggest that ideological and strategic variables are better predictors of adaptations to jolts than are structural variables or measures of organizational slack. Although abrupt changes in environments are commonly thought to jeopardize organizations, environmental jolts are found to be ambiguous events that offer propitious opportunities for organizational learning, administrative drama, and introducing unrelated changes.     

Learning Through Joint Ventures: A Framework Of Knowledge Acquisition

Joint ventures (JVs) are becoming an increasingly important organizational form in international business. When JVs are formed, valuable learning opportunities may be created for the venture partners. The primary objective in this paper is to explore the conditions under which firms exploit JV learning opportunities through the acquisition of knowledge. A framework of knowledge acquisition by JV partner firms is proposed. Using JV partner organizations as the primary level of analysis, the paper identifies various factors that influence the acquisition of learning, its value to the learning organization, and the migration of knowledge from the JV to the parent. Two firm specific learning-based concepts are developed: alliance knowledge accessibility and knowledge acquisition effectiveness.     

The Emerging Role of Higher Education in Educating and Assessing Future Leaders for Disaster Relief

As the frequency and intensity of disasters throughout the world increase, so do the demands for humanitarian relief and the need for effective disaster‐based leadership training. Numerous organizations are poised to act in response to disasters that necessitate a significant amount of leadership and logistics support. Although there are many entities often involved in disaster relief, the symposium outlines the emerging role higher education can play in improving response outcomes, not only by providing “boots on the ground” but also by developing skilled disaster relief leaders in both curricular and cocurricular venues. Institutions of higher education provide experiential learning opportunities offering critical service for immediate disaster relief while developing the skills of future leaders. During these experiences, students develop disaster‐based leadership and logistics acumen. Similarly, programmatic assessment is critical in measuring student leadership growth during the experiential learning process of providing disaster relief. The education and assessment of these trainings provides an important dimension of disaster leadership as it examines the leaders in action and fosters a commitment to adequately prepare them to lead in a time of disaster. A qualitative approach is recommended for assessing experiential leadership learning to better inform teaching and to cultivate future disaster leadership that meets ongoing global needs.     

Multi-player dynamic game model for Bitcoin transaction bidding prediction

With the rapid rise of cryptocurrencies, it has become an urgent problem to realize the flat use of digital currency, with making it really put into use, and giving full play to its utility in the current economic market. This paper innovatively takes the maximization of user benefit as the key point to predict transaction bidding price combining dynamic game theory. The bidding price of user transaction not only refers to historical transactions, but also considers the impact on future subsequences, and the result describes the interaction between transactions in detail. Also this paper proposes a method to express user satisfaction and establishes a user benefit model accordingly, so as to ensure the transaction is packaged successfully to the greatest extent within the acceptable range of transaction pricing. Finally this paper compares the proposed model with conventional machine learning prediction algorithms, finding that when user does not participate in the trading for the first time, the prediction effect of this proposal is better than that of machine learning over small data sets, moreover superior to machine learning methods in prediction accuracy and sensitivity, with a lower time complexity.     

Forecasting football results and exploiting betting markets: The case of “both teams to score”

The continuous growth of available football data presents unprecedented research opportunities for a better understanding of football dynamics. While many research works focus on predicting which team will win a match, other interesting questions, such as whether both teams will score in a game, are still unexplored and have gained momentum with the rise of betting markets. With this in mind, we investigate the following research questions in this paper: “How difficult is the ‘both teams to score’ (BTTS) prediction problem?”, “Are machine learning classifiers capable of predicting BTTS better than bookmakers?”, and “Are machine learning classifiers useful for devising profitable betting strategies in the BTTS market?”. We collected historical football data, extracted groups of features to represent the teams’ strengths, and fed these to state-of-the-art classification models. We performed a comprehensive set of experiments and showed that, although hard to predict, in some scenarios it is possible to outperform bookmakers, which are robust baselines per se. More importantly, in some cases it is possible to beat the market and devise profitable strategies based on machine learning algorithms. The results are encouraging and, besides shedding light on the problem, may provide novel insights for all kinds of football stakeholders.     

Working and organizing in the age of the learning algorithm

Learning algorithms, technologies that generate responses, classifications, or dynamic predictions that resemble those of a knowledge worker, raise important research questions for organizational scholars related to work and organizing. We suggest that such algorithms are distinguished by four consequential aspects: black-boxed performance, comprehensive digitization, anticipatory quantification, and hidden politics. These aspects are likely to alter work and organizing in qualitatively different ways beyond simply signaling an acceleration of long-term technology trends. Our analysis indicates that learning algorithms will transform expertise in organizations, reshape work and occupational boundaries, and offer novel forms of coordination and control. Thus, learning algorithms can be considered performative due to the extent to which their use can shape and alter work and organizational realities. Their rapid deployment requires scholarly attention to societal issues such as the extent to which the algorithm is authorized to make decisions, the need to incorporate morality in the technology, and their digital iron-cage potential.     

Do Opportunities for Formal Learning Stimulate Follow‐Up Participation in Informal Learning? A Three‐Wave Study

Informal learning is an important source of employee adaptability and expertise, yet it is unclear how it may be encouraged through human resource management (HRM) practices. In this study, we investigate how opportunities for formal learning may be used to stimulate short‐ and long‐term participation in informal learning. In addition, we examine whether HRM system strength intensifies the relationship between opportunities for formal learning and informal learning. Using a sample of 430 respondents in 52 teams within six Dutch vocational and educational training schools, we adopt a longitudinal design to examine two types of autonomous informal learning activities (reflection and keeping up to date), and three collaborative activities (asking for feedback, knowledge sharing and innovative behavior) over two years. Opportunity for formal learning was positively related to short‐ and long‐term participation in informal learning activities, with the exception of long‐term innovation. Moreover, HRM system strength intensified these relationships. Managerial implications of these findings for encouraging informal learning activities at work are discussed. © 2016 Wiley Periodicals, Inc.     

Play at work,learning and innovation

ABSTRACT

Suggesting a virtuous triangle constituting public service innovation of new governances, innovation and learning, the paper examines how and why a particular mode of learning occurs: that of play. Having identified an absence of research literature on play as a catalyst for new ideas in public services, the paper argues that the diversified nature of public services and disciplinary intermixing offers fertile ground for playing with new service ideas. Our conception of play avoids functional interpretations, such as Amabile or individualizing the results of play and instead draws upon Vygotsky’s social learning theory to conceptualize play as a group activity from which new ideas emerge and suggest a new framework for understanding purposive play at work and the contribution it can make to public service innovation.     

The Cognitive and Behavioural Dimensions of Organizational Learning in a Merger: an Empirical Study

This paper addresses the links between cognitive and behavioural learning at both a theoretical and empirical level. An integrative model is developed which identifies a number of conceptually distinct learning modes and which incorporates the tacit aspect of knowledge. An empirical illustration of this model is provided in the form of a case study of a merger of two companies in the BSN-Danone Group. Mergers provide firms with opportunities for shared learning but this study also identifies several barriers to learning. The managerial implications of an integrated approach to organizational learning are also discussed.     

《单片机应用技术》课程实验实训项目改革探索

单片机是一门实践性和技术性很强的课程,如何设计激发学生的学习兴趣、让学生能运用学过的知识创造发挥的实验实训项目在单片机教学过程中非常重要。本文按"任务驱动法"对实验实训内容进行调整,合理安排实验实训内容,并针对不同的项目采用不同的教学方法,借此探索在传统实验方式上的教学课程改革,加强学生实践能力和创新精神培养。     

The role of mentoring in the learning development of the novice entrepreneur

It has been recognized that learning outcomes can be divided into three general categories: Cognitive, skill-based, and affective learning. Moreover, public organizations offer mentoring-inspired support programs to novice entrepreneurs starting a business. This study explores entrepreneurial learning through mentoring. We have coded 53 learning outcomes acquired by novice entrepreneurs through their mentoring relationship. These learning outcomes were analysed using the three general categories and linked with the mentor’s way of promoting learning. Cognitive learning accounts for 62% of total learning outcomes and affective learning makes up 35.9%, which leaves skill-based learning in a marginal position. In addition, methods used by the mentor to ensure mentee learning affect learning content. Mentees were also asked to describe the benefits of their mentoring relationship through discussion groups. From a cognitive learning standpoint, benefits reported by entrepreneurs include an increase in management knowledge and skills, improved vision for their business venture and identifying new opportunities. Benefits resulting from affective learning include a greater sense of self-efficacy, validation of one’s entrepreneurial self-image and a lowered sense of solitude, all factors that could ultimately influence entrepreneur resilience. These results reveal the scope and limitations of mentoring as means to support learning.     

Artificial Intelligence and Human Flourishing

The polarization of the debate about artificial intelligence (AI) pulls in two mutually exclusive directions of either complete takeover of future jobs by omnipotent algorithms or an absolute bliss with robots at work while humans reap the benefits of endless vacation. Add this to conflicting views of work as either a disutility to be minimized or as an essential component in human flourishing, and it is no wonder a wide range of views are expressed on AI and human flourishing. The literature, from Smith to Keynes and beyond, offers some initial methodological guidance. Still, the true social and economic implications of an AI-type environment in production and labor markets are yet to be fully understood. This article argues that neither of the predictions are realistic. Instead, the global economy may be passing, albeit at a faster speed, through a phase of technological change, similar to those experienced before. While a nuanced balance is emerging, with an emphasis on human skills in future employment, the benefits may not be equitably distributed, as equality of opportunities for human development may not be reachable, though visible, in the AI-driven society. Hence, as firms seek efficiency gains, much weight is shifted onto governments and quasi-private entities in maintaining decent living standards conducive to human flourishing in unprecedented times of the COVID-19 pandemic. The article reviews various popular concerns and advances new public policy measures aimed at tackling some of the immediate fears of automation.     

Optimal consumption under uncertainty,liquidity constraints,and bounded rationality

I study how boundedly rational agents can learn a “good” solution to an infinite horizon optimal consumption problem under uncertainty and liquidity constraints. Using an empirically plausible theory of learning I propose a class of adaptive learning algorithms that agents might use to choose a consumption rule. I show that the algorithm always has a globally asymptotically stable consumption rule, which is optimal. Additionally, I present extensions of the model to finite horizon settings, where agents have finite lives and life-cycle income patterns. This provides a simple and parsimonious model of consumption for large agent based models.     

Optimization of rain gauge networks—A systematic literature review

Rain gauge is the standard instrument for ground-based precipitation measurements and rain gauge networks play an important role in the monitoring of disasters caused by heavy rainfall, such as flooding and landslides. Many authors have contributed to the research of sophisticated methods and techniques for the design of optimal rain gauge networks, including guidelines for the placement of the devices based on the meteorological and physiographic factors of the study area, however, there is not a consensus on the best procedure to locate these equipment. In fact, there is a rapid growing interest and need for the improvement of monitoring networks and the optimization algorithms and solvers allowed a rapid evolution toward more efficient models. This study presents a systematic literature review (SLR) of studies on the optimization of rain gauge networks using the PRISMA protocol, detailing the study areas, methods, datasets and models used. To the best of authors’ knowledge, the latest thorough SLR on this subject was published in 2009 by the time of development of the authors’ research, however, the amount of research on this topic has grown significantly since then and optimization techniques have undergone a compelling evolution. Results indicate a wide variety of applications, objectives, methods, models and algorithms aimed at optimizing rain gauge networks. Conclusions are drawn in the light of the opportunities for future research on this topic.