Integration of Machine Learning Insights into Organizational Learning: A Case of B2B Sales Forecasting

2016 ◽  
pp. 71-85 ◽  
Author(s):  
Marko Bohanec ◽  
Mirjana Kljajić Borštnar ◽  
Marko Robnik-Šikonja
Keyword(s):  
Machine Learning ◽  
Organizational Learning

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.

Artificial intelligence in the workplace – A double-edged sword

2020 ◽  
Vol 37 (5) ◽  
pp. 253-265
Author(s):  
Uta Wilkens
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Organizational Learning ◽  
Learning Theory ◽  
Practical Implication ◽  
Job Design ◽  
Macro Level ◽  
Learning Approaches ◽  
Content Type ◽  
Meso Level

PurposeThe aim of this paper is to outline how artificial intelligence (AI) can augment learning process in the workplace and where there are limitations.Design/methodology/approachThe paper is a theoretical-based outline with reference to individual and organizational learning theory, which are related to machine learning methods as they are currently in use in the workplace. Based on these theoretical insights, the paper presents a qualitative evaluation of the augmentation potential of AI to assist individual and organizational learning in the workplace.FindingsThe core outcome is that there is an augmentation potential of AI to enhance individual learning and development in the workplace, which however should not be overestimated. AI has a complementarity to individual intelligence, which can lead to an advancement, especially in quality, accuracy and precision. Moreover, AI has a potential to support individual competence development and organizational learning processes. However, a further outcome is that AI in the workplace is a double-edged sword, as it easily shows reinforcement effects in individual and organizational learning, which have a backside of unintended effects.Research limitations/implicationsThe conceptual outline makes use of examples for illustrating phenomenon but needs further empirical analysis. The research focus on the meso level of the workplace does not fully refer to macro level outcomes.Practical implicationsThe practical implication is that it is a matter of socio-technical job design to integrate AI in the workplace in a valuable manner. There is a need to keep the human-in-the-loop and to complement AI-based learning approaches with non-AI counterparts to reach augmentation.Originality/valueThe paper faces workplace learning from an interdisciplinary perspective and bridges insights from learning theory with methods from the machine learning community. It directs the social science discourse on AI, which is often on macro level to the meso level of the workplace and related issues for job design and therefore provides a complementary perspective.

scholarly journals In Search of Inspiration: External Mobility and the Emergence of Technology Intrapreneurs

2021 ◽  
Author(s):  
Weiyi Ng ◽  
Eliot L. Sherman
Keyword(s):  
Machine Learning ◽  
Organizational Learning ◽  
Research Design ◽  
Learning Theory ◽  
Turnover Rate ◽  
Job Descriptions ◽  
Exact Matching ◽  
Organizational Learning Theory ◽  
Recent Scholarship ◽  
Product Manager

Recent scholarship has established several ways in which external hiring—versus filling a role with a comparable internal candidate—is detrimental to firms. Yet, organizational learning theory suggests that external hires benefit firms: by importing knowledge that is unavailable or obscured to insiders and applying it toward experimentation and risky recombination. Accordingly and consistent with studies of learning by hiring and innovation, we predict that external hires are at greater risk of intrapreneurship than internal hires. We test this prediction via a study of product managers in large technology companies. We use machine learning to operationalize intrapreneurship by comparing product manager job descriptions with the founding statements of venture-backed technology entrepreneurs. Our research design employs coarsened exact matching to balance pretreatment covariates between product managers who arrived at their roles internally versus externally. The results of our analysis indicate that externally hired product managers are substantially more intrapreneurial than observably equivalent internal hires. However, we also find that intrapreneurial product managers have a higher turnover rate, an effect that is primarily driven by external hires. This suggests that hiring for intrapreneurship may be a difficult strategy to sustain.

Decision-making framework with double-loop learning through interpretable black-box machine learning models

2017 ◽  
Vol 117 (7) ◽  
pp. 1389-1406 ◽  
Author(s):  
Marko Bohanec ◽  
Marko Robnik-Šikonja ◽  
Mirjana Kljajić Borštnar
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Decision Making ◽  
Organizational Learning ◽  
Decision Making Process ◽  
Organizational Setting ◽  
Business Decision ◽  
Content Type ◽  
Industry Standard ◽  
General Explanation

Purpose The purpose of this paper is to address the problem of weak acceptance of machine learning (ML) models in business. The proposed framework of top-performing ML models coupled with general explanation methods provides additional information to the decision-making process. This builds a foundation for sustainable organizational learning. Design/methodology/approach To address user acceptance, participatory approach of action design research (ADR) was chosen. The proposed framework is demonstrated on a B2B sales forecasting process in an organizational setting, following cross-industry standard process for data mining (CRISP-DM) methodology. Findings The provided ML model explanations efficiently support business decision makers, reduce forecasting error for new sales opportunities, and facilitate discussion about the context of opportunities in the sales team. Research limitations/implications The quality and quantity of available data affect the performance of models and explanations. Practical implications The application in the real-world company demonstrates the utility of the approach and provides evidence that transparent explanations of ML models contribute to individual and organizational learning. Social implications All used methods are available as an open-source software and can improve the acceptance of ML in data-driven decision making. Originality/value The proposed framework incorporates existing ML models and general explanation methodology into a decision-making process. To the authors’ knowledge, this is the first attempt to support organizational learning with a framework combining ML explanations, ADR, and data mining methodology based on the CRISP-DM industry standard.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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