Effective incomplete contracts and milestones in market-distant R&D collaboration

R&D outsourcing is often conducted during the early, market-distant stages of the innovation process. However, the main obstacle to this potentially efficient interfirm specialization is the high danger of moral hazard. Most organizational mechanisms fail to control that type of opportunism because of information asymmetries, even ex post. In the theory of incomplete contracts, this problem is mitigated by assigning the control rights to the supplier. To date, empirical studies have mainly investigated the interfirm distribution of the control rights. However, we do not know yet which concrete control right is crucial with regard to supplier opportunism, which is the decisive dependent variable. Our study addresses this research gap. For the first time, we extend the empirical focus from biotechnology and pharmaceutical firm alliances to a cross-industry sample of 113 collaboration cases. The results show the effectiveness of contracts that ex ante assign patent ownership rights to the supplier. The findings are also relevant for management practice because the majority of practitioners do not use this contract type yet, although there is no sign of an effective alternative.     

Observing Interprofessional Collaboration: Impact on Attitude and Knowledge Acquisition

Interprofessional collaboration (IPC) in the medical field is an important factor for good clinical outcomes and should be taught as early as in undergraduate medical education. Since implementing IPC training is an organizational challenge, students are often limited in their opportunities to experience real-life IPC. Therefore, an approach where students observe successful IPC activities of role models in an applied anatomical format was proposed. It was studied whether observing IPC activities in undergraduate anatomical education has an impact on both students' attitude toward IPC and on knowledge acquisition. Further, it was examined whether the attitudes and knowledge of students from different medical disciplines were influenced in different ways. Therefore, 75 medical students and thirty-eight physiotherapy students participated in a study with the task of observing a live broadcast of an interprofessional teaching session. Participants were asked about their attitudes toward interprofessional learning, their evaluation of professional responsibilities, and their profession-specific knowledge before and after observing the IPC session. The participants' attitude toward interprofessional learning improved for both groups of students. Moreover, students of physiotherapy adjusted their evaluation of their own and others' professional responsibilities after observing IPC. In both student groups, knowledge increased, in particular, with respect to the field of knowledge in other professions. So, observing IPC can modify students' attitudes and support knowledge acquisition. The implementation of IPC observations provides students from various healthcare disciplines with a clearer impression of professionals' responsibilities and gives learners the opportunity to acquire knowledge from healthcare fields unfamiliar to them.     

体育项目中的训练与过度训练特征

目的:多种症状都支持对过度训练(OT)的探查。测量血清尿素(SU)和血清肌氨酸激酶(CK)值,除了对优秀运动员进行诊断和分析之外,也用于对运动员日常训练状态进行监控。因此,我们将根据不同运动员,同一运动员的不同状态,对这些指标的意义进行检验。方法:测试时象为具有国际水平的赛艇运动员,所有数据均来自于对这些运动员训练过程的测试。结果:6981例血清尿素测试样本(男运动员717人,女运动员285人)表明, 数据呈现出一种轻微不对称的正态分布态势(男运动员80％,5～7mmol·L-1;女运动员75％, 4～6mmol·L-1)。女运动员的数值比男运动员约低1．5mmol·L-1,个体差异较大,因此以 8．3mmol·L-1(男运动员)和7．0mmol·L-1(女运动员)作为判断运动员过度训练的固定阈值是不可靠的。在运动训练中,肌氨酸激酶(CK)也已被作为判定肌肉紧张的重要参数进行检测和评价。2790例肌氨酸激酶测试样本(男运动员497人,女运动员350人)显示,数据的频率分布为不对称的正态分布,在100—250U·L-1的区域内具有明显偏向高值区域的趋势,在250～ 350U·L-1和1000～2000U·L-1范围内分布频数明显升高。男运动员的最高值为3000U·L- 1,女运动员最高值为1150U·L-1,个体差异显著。血清肌氨酸激酶水平长期较低的运动员显示出小的数值变化;血清肌氨酸激酶水平长期较高的运动员在数值上显示出显著的变化波动。结论:这两个参数也许都有助于从大量样本中测定个人数值基准线。对这两个参数的测量至少应该在标准条件下每3天进行一次。如果在一段时间的训练(2—4天)后同时观察到参数数值显著升高和训练耐受力降低,则表明机体的分解／合成代谢活动或训练耐受力不足的可能性升高。     

What goes before the CART? Introducing classification trees with Arbor and CODAP

This volume is largely about nontraditional data; this paper is about a nontraditional visualization: classification trees. Using trees with data will be new to many students, so rather than beginning with a computer algorithm that produces optimal trees, we suggest that students first construct their own trees, one node at a time, to explore how they work, and how well. This build-it-yourself process is more transparent than using algorithms such as CART; we believe it will help students not only understand the fundamentals of trees, but also better understand tree-building algorithms when they do encounter them. And because classification is an important task in machine learning, a good foundation in trees can prepare students to better understand that emerging and important field. We also describe a free online tool—Arbor—that students can use to do this, and note some implications for instruction.