Coordinating Human and Machine Learning for Effective Organization Learning

MIS Quarterly ◽  
2021 ◽  
Vol 45 (3) ◽  
pp. 1581-1602
Author(s):  
Timo Sturm ◽  
◽  
Jin Gerlacha ◽  
Luisa Pumplun ◽  
Neda Mesbah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organizational Learning ◽  
Organization Learning ◽  
Agent Based ◽  
Scarce Resources ◽  
Turbulent Environments ◽  
Explorative Learning ◽  
New Ideas ◽  
Effective Organization ◽  
Stock Of Knowledge

With the rise of machine learning (ML), humans are no longer the only ones capable of learning and contributing to an organization’s stock of knowledge. We study how organizations can coordinate human learning and ML in order to learn effectively as a whole. Based on a series of agent-based simulations, we find that, first, ML can reduce an organization’s demand for human explorative learning that is aimed at uncovering new ideas; second, adjustments to ML systems made by humans are largely beneficial, but this effect can diminish or even become harmful under certain conditions; and third, reliance on knowledge created by ML systems can facilitate organizational learning in turbulent environments, but this requires significant investments in the initial setup of these systems as well as adequately coordinating them with humans. These insights contribute to rethinking organizational learning in the presence of ML and can aid organizations in reallocating scarce resources to facilitate organizational learning in practice.

scholarly journals The Effect of Mediation Work Engagement to Procedural Justice and Organizational Learning on the Innovative Behavior

2020 ◽  
Vol 9 (2) ◽  
pp. 152-160
Author(s):  
Fara Kartika Sari ◽  
Palupiningdyah Palupiningdyah
Keyword(s):  
Organizational Learning ◽  
Procedural Justice ◽  
Work Engagement ◽  
Sampling Technique ◽  
Procedural Fairness ◽  
Positive Influence ◽  
Innovative Behavior ◽  
Organization Learning ◽  
New Ideas ◽  
Work Attachment

The purpose of this study was to determine the effect of procedural fairness and organization learning on innovative behavior through work engagement. The population in this study were all batik craftsmen in Semarang City IKM Batik, totaling 165 people. Based on this population 117 respondents were taken by proportional random sampling technique. Data collection in this study used a questionnaire, observation and interviews. The analytical method used is SPSS IBM Statistics 25. The results of this study indicate that procedural justice has positive and significant influence on innovative behavior, organizational learning can have a positive and significant effect on innovative behavior and work engagement, work engagement can have a positive and significant effect on innovative behavior, then work engagement can mediate the positive influence of procedural justice and organizational learning on innovative behavior. The conclusion of this research is procedural fairness,organizational learning and work engagemEnt can increase innovative behavior. Furthermore work attachment can mediate the influence of procedural fairness and organization learning on innovative behavior. Suggestions for IKM batik management are to have emotional closeness such as the availability of leaders to listen to the complaints of employees, the existence of activities such as workshops and exhibitions on a regular basis to trigger increased knowledge so that it is easier to create new ideas.

School Environment as a Determinant of Organizational Learning Support

2019 ◽  
Vol 9 (1) ◽  
pp. 20-44
Author(s):  
Valentína Šuťáková ◽  
Janka Ferencová ◽  
Martina Kosturková
Keyword(s):  
Qualitative Research ◽  
Organizational Change ◽  
Organizational Learning ◽  
School Environment ◽  
Psychological Safety ◽  
School Management ◽  
Learning Support ◽  
Open Communication ◽  
New Ideas ◽  
Learning Research

Organizational learning as a strategic tool for organizational change and stabilization of success has been discussed in a school environment since the 1990s. Its importance is increasing in the context of dynamic development of the society and the need to flexibly adapt to constant changes. Organizational learning research focuses on analyzing factors that are important to organizational learning and adaptability. The study aim was to examine a school environment as a determinant of organizational learning support. We chose a design of qualitative research, in which a group interview with 32 teachers from four schools was carried out. Based on the interviews we specified five categories of organizational learning support – psychological safety, open communication, cooperation, openness to new ideas, engagement and participation. Through an analysis of participants‘ responses, we identified the most significant barriers to organizational learning in the environment of selected schools. Their recognition has made it possible to formulate recommendations related to school management and to the promotion of organizational learning at schools.

PODC 2020 Review

2021 ◽  
Vol 51 (4) ◽  
pp. 75-81
Author(s):  
Ahad Mirza Baig ◽  
Alkida Balliu ◽  
Peter Davies ◽  
Michal Dory
Keyword(s):  
Machine Learning ◽  
Distributed Computing ◽  
Keynote Speaker ◽  
Lively Discussion ◽  
Theoretical Understanding ◽  
New Directions ◽  
New Ideas ◽  
New Challenges ◽  
The Impact ◽  
Distributed Machine Learning

Rachid Guerraoui was the rst keynote speaker, and he got things o to a great start by discussing the broad relevance of the research done in our community relative to both industry and academia. He rst argued that, in some sense, the fact that distributed computing is so pervasive nowadays could end up sti ing progress in our community by inducing people to work on marginal problems, and becoming isolated. His rst suggestion was to try to understand and incorporate new ideas coming from applied elds into our research, and argued that this has been historically very successful. He illustrated this point via the distributed payment problem, which appears in the context of blockchains, in particular Bitcoin, but then turned out to be very theoretically interesting; furthermore, the theoretical understanding of the problem inspired new practical protocols. He then went further to discuss new directions in distributed computing, such as the COVID tracing problem, and new challenges in Byzantine-resilient distributed machine learning. Another source of innovation Rachid suggested was hardware innovations, which he illustrated with work studying the impact of RDMA-based primitives on fundamental problems in distributed computing. The talk concluded with a very lively discussion.

scholarly journals How Do Prior Ties Affect Learning by Hiring?

2018 ◽  
Vol 46 (2) ◽  
pp. 287-320 ◽  
Author(s):  
Vivek Tandon ◽  
Gokhan Ertug ◽  
Gianluca Carnabuci
Keyword(s):  
Organizational Learning ◽  
Knowledge Base ◽  
Learning Outcomes ◽  
Theoretical Understanding ◽  
Explorative Learning ◽  
Managerial Implications ◽  
Exploitative Learning ◽  
Using Data ◽  
Electronics And Electrical ◽  
New Hires

Research has shown that hiring R&D scientists from competitors fosters organizational learning. We examine whether hiring scientists who have many collaborative ties with the hiring firm prior to the mobility event produces different learning outcomes than hiring scientists who have few or no such ties. We theorize that prior ties reduce explorative learning and increase exploitative learning. Namely, we posit that prior ties lead the hiring firm to focus on that part of a new hire’s knowledge with which they are already familiar and that they help appropriate the new hire’s newly generated knowledge. At the same time, prior ties induce new hires to search locally within the hiring firm’s knowledge base and to produce more incremental, lower-impact innovations. Using data on R&D scientists’ mobility in the Electronics and Electrical Goods industry, we find broad support for our hypotheses. Our results extend our theoretical understanding of learning-by-hiring processes and bear practical managerial implications.

The Pedagogical Considerations in the Design of Virtual Worlds for Organization Learning

2014 ◽  
pp. 1390-1409
Author(s):  
C. Candace Chou ◽  
Rama Kaye Hart
Keyword(s):  
Leadership Development ◽  
Organizational Learning ◽  
Virtual Worlds ◽  
Second Life ◽  
Relevant Literature ◽  
Student Perspectives ◽  
Learning Theories ◽  
Organization Learning ◽  
Immersive Learning ◽  
Pedagogical Design

An increasing number of organizations have established presences in Second Life or virtual worlds for organizational learning. The types of activities range from staff training, annual meetings, to leadership development and commercial transactions. This chapter reviews relevant literature on how virtual worlds, especially Second Life, are utilized for organizational learning. The discussions include leveraging the affordances of virtual worlds for learning, integrating design principles of 3D immersive learning, and examining examples of actual workplace learning in virtual worlds. Specific emphasis will be placed on the translation of applicable learning theories into the pedagogical design of virtual worlds. Furthermore, the chapter examines student perspectives of an actual course on immersive learning that took place in Second Life. Student perspectives are summarized in six strands: challenging and informative learning, engagement, activity structures, transformation, collaborative and democratic participation, and new opportunities. The six themes are important factors for designers of 3D learning environments to ensure quality immersive learning experiences.

Theory, Data, and Methods

2019 ◽  
pp. 156-202
Author(s):  
Eda Ustaoglu ◽  
Arif Çagdaş Aydinoglu
Keyword(s):  
Machine Learning ◽  
Land Use ◽  
Information System ◽  
Land Use Change ◽  
Model Development ◽  
Agent Based ◽  
Change Models ◽  
Multi Scale ◽  
Land Use Dynamics ◽  
Scale Characteristics

Land-use change models are tools to support analyses, assessments, and policy decisions concerning the causes and consequences of land-use dynamics, by providing a framework for the analysis of land-use change processes and making projections for the future land-use/cover patterns. There is a variety of modelling approaches that were developed from different disciplinary backgrounds. Following the reviews in the literature, this chapter focuses on various modelling tools and practices that range from pattern-based methods such as machine learning and GIS (Geographic Information System)-based approaches, to process-based methods such as structural economic or agent-based models. For each of these methods, an overview is given for the advances that have been progressed by geographers, natural and economy scientists in developing these models of spatial land-use change. It is noted that further progress is needed in terms of model development, and integration of models operating at various scales that better address the multi-scale characteristics of the land-use system.

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