Learning Literature Research Articles

Under Pressure: External Monitor, Managerial Attention and Organizational Learning from Major Errors

Research on organizational learning from serious errors has uncovered several influences related to a firm’s own experiences and those of its peers. While the learning literature therefore acknowledges that the external environment affects organizational learning, research has largely overlooked the possibility that organizations other than competitors might also shape learning by the focal firm. We explore this proposition by studying the collective influence of an essential monitor (financial analysts) on repeat incidences of a serious organizational error (major oil spills). Specifically, we theorize that monitors apply pressure by setting expectations in the immediate aftermath of an error (encouraging learning from that error by directing managerial attention toward safety), but that these effects are counteracted by subsequent pressure to meet a different expectation (encouraging forgetting by diverting managerial attention away from safety and toward profit). We also theorize that analysts further shape organizational learning from errors by conditioning what we know about both experiential and vicarious learning. We test this theory by studying the effects of evolving analyst pressures on major oil spills in the U.S. by firms in the oil and gas industry, from 1980 to 2016. By showing that analysts apply differential pressures to firms following a serious error (immediately and, then, subsequently), this study highlights the role of external monitors in encouraging both organizational learning and forgetting due to socially set expectations that shift the attention of managers among competing foci in ways that affect the likelihood of repeat serious errors.

Biodynamic in California: Organizational Cognition and Learning with Complexity

The convergence of social and environmental crises has urged organizations and institutions to apply systems-thinking principles as a way to better attend to ongoing issues and their inherent complexity. This qualitative empirical study inductively investigates the types of organizational-level cognition apparent in 20 sustainable wine producers in California applying different agricultural and organizational practices. We offer developments in the organizational cognition and learning literatures by presenting systems-thinking in the face of complexity as a unique empirical contribution. We differentiate between holistic (systems) and particularist (linear) cognition and dynamic ways of learning in organizations. Through semi-structured interviews, field observations, and secondary data from their website communications, we elaborate upon emergent findings and generalize a model of enacted cognition and organizational learning in a bidirectional manner with institutions. By focusing on producers with sustainable orientations, many implications related to responding to complex social and environment issues by applying holistic ontologies arise for further contemplation. Our insights serve as a foundation for understanding how organizations think in while dealing with ongoing complexity and long-term challenges, including climate change, biodiversity loss, and social inequality.

The Effect of Acquisitions on Environmental Performance: Evaluating the Role of Primary Stakeholders

This study examines how acquisitions influence corporate environmental performance by integrating theoretical notions from organizational learning and stakeholder literature. We posit that firms can improve environmental performance following acquisitions by learning from their targets. However, this depends on the environmental knowledge and capabilities of the target relative to those of the acquiring firm. We further suggest that change in environmental performance following acquisitions does not only depend on the target firms, but also on the acquirers’ primary stakeholder groups. We test our hypotheses by analyzing longitudinal data on 380 acquisitions of public, US-based acquirers. We find that acquirers with a strong orientation towards customers, employees and suppliers are more likely to learn from their acquisition targets. Furthermore, we show that the reaction of shareholders to acquisition announcements also determines the extent to which firms improve environmental performance following acquisitions. Our results underline the importance of primary stakeholders in achieving environmental performance improvements following strategic actions. With this research, we contribute to literature on M&As by going beyond the implications of acquisitions on financial firm value. We further contribute to stakeholder literature by providing insights on the role of primary stakeholders for organizational learning.

Who is Failing? The Impact of Failure of Generalists vs. Specialists on New Product Introductions

Firms learn from the failure of others, but unlike their own failure, the cause of others’ failure is ambiguous, i.e. open to varying interpretation. Varying interpretation, in turn, is likely to lead to differences in what firms learn and the effect it has on their future behavior. This paper investigates whether or not a characteristic of failing firms, specifically their scope, affects observing firms’ strategic choices in the future. This study thus seeks to improve our understanding of a firm’s strategic choice between exploration and exploitation activities following the failure of others, highlighting the role of firm scope or breadth of operations, by drawing on attribution theory and organizational learning literature.

Explaining the Length of the Due Diligence Process in Technology Acquisitions

Drawing on information economics, the theory of the firm and the organizational learning literature, we theorize about the probability of length of the due diligence process. In particular, we examine to what extent uncertainty regarding the value of a target’s resources and differences between the acquiring and target firms lengthen the acquirer’s due diligence effort. Given that we focus on the (target) technological resources we investigate the role of technological uncertainty and absorptive capacity proxying them by the number of pending patent applications of target firms and the technological distance between the two firms’ patent portfolio, respectively. Further, we argue that business relatedness (similarity) reduces the understanding gap. Using a longitudinal data on completed and non-completed technology acquisitions in the UK, we find a significantly positive effect of targets’ pending patent applications on due diligence length that is amplified by technological distance but reduced by business relatedness. Our study contributes to the M&A literature by revealing that higher information gap and uncertainty prolong the due diligence process of the acquirers in the hope of effectively evaluating prospective target firms.

An Examination of the Relationship between Organizational Memory and Absorptive Capacity

The organizational memory (OM) and absorptive capacity (AC) constructs surfaced in management science the same year. Yet, AC has received considerably more attention in the organizational learning literature, even though both constructs are important to understanding and explaining organizational learning and change. We suggest that because both constructs came from cognitive psychology and share the notion of individual memory. In this paper, we make a clear distinction between them. Importantly, we suggest how these two constructs relate to one another, how they fit in the nomological network, and their distinct role in affecting organizational change and learning. In doing so, we generate testable propositions for empirical examination in future research.

Bridging the gap between autonomous and predetermined paradigms: The role of sampling in evaluative learning.

Whereas most evaluative learning paradigms remove participants' autonomy over the information they receive, other research traditions have demonstrated that information sampling has an important role in learning. We investigated the impact of information sampling on a central evaluative learning paradigm: evaluative conditioning. We compared a traditional evaluative conditioning paradigm with a paradigm in which participants have autonomy over the stimulus pairings they receive. Participants in the high-autonomy condition showed a strong preference for positively paired CSs. Nevertheless, the strength of evaluative conditioning effects was independent of autonomy. Moreover, high-autonomy participants, but not their low-autonomy counterparts, demonstrated a relationship between sampling frequency and evaluations, in line with the interpretation that sampled stimuli become more positive, whereas ignored stimuli become more negative over the course of the learning phase. The present research provides a cornerstone for integrating several research traditions within and beyond the evaluative learning literature, providing a foundation for new insights and more comprehensive theories of evaluative learning. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Literature as one of the fascinating lessons for junior high school students

Literature is written work, especially those considered superior or lasting artistic merit. Literature is one of the important things in the world and life. An assist contention against teaching literature was that modern innovation and social hones within the tall in West had rendered—or were on the point of rendering—reading and composing pitifully obsolete. This research aims to explore the students' understanding of English literature according to the expert and one their knowledge and the necessary type of literature as well. Further, by implementing a descriptive qualitative method design which was aimed to give an original view of variable, indication, or condition. The college student also defined that literature as a guide. And some junior high school student said that literature is an artwork in the form of written phrases that contains the spirit, sighing, etc. They honestly claimed that learning English literature made them increase their knowledge about English literature which was amazing. To solve the challenges, the college students recalled their moments and experience when learning literature at junior high school and argued about English literature that was so boring and wasted time. Further research can be done to explore the perception of English literature in junior high school from junior high school students and the college student's point of view.

MULTILITERACY LEARNING IN NOMMENSEN THEATER FILMS IN BATAK

Multiliteracy learning in recent years has become very interesting because by studying two or more literacy materials we can see how they relate to other literacy materials. Especially when it comes to learning literature and language, the film Nommensen in Batak land is a theatrical film that has been accommodated and shown especially for the Batak community in North Sumatra and outside the Tapanuli area. The film is usually only made in memory of Nommensen. However, in this study, the film will be studied so that it can produce a multiliterate assessment contained in the film. The results of this study indicate that complete multiliteracy learning can be learned even in certain drama or cultural context films.

Transcranial direct current stimulation (tDCS) eliminates the other-race effect (ORE) indexed by the face inversion effect for own versus other-race faces

We investigate here individuals’ reduced ability to recognise faces from other racial backgrounds, a robust phenomenon named the other-race effect (ORE). In this literature the term “race” is used to refer to visually distinct ethnic groups. In our study, we will refer to two of such groups: Western Caucasian (also known as White European) and East Asian e.g., Chinese, Japanese, Korean. This study applied the tDCS procedure (double-blind, 10 min duration, 1.5 mA intensity, targeting Fp3 location), developed in the perceptual learning literature, specifically used to remove the expertise component of the face inversion effect (FIE), which consists of higher recognition performance for upright than inverted faces. In the tDCS-sham condition (N = 48) we find a robust ORE i.e., significantly larger FIE for own versus other-race faces due to higher performance for upright own-race faces. Critically, in the anodal-tDCS condition (N = 48) the FIE for own-race faces was significantly reduced compared to sham due to impaired performance for upright faces thus eliminating the cross-race interaction index of the ORE. Our results support the major role that perceptual expertise, manifesting through perceptual learning, has in determining the ORE indexed by the FIE.

A practical analysis of blended training efficacy on organizational outcomes

PurposeThe purpose of this paper is to contribute to the literature of blended learning by practically implementing best practices in employee training.Design/methodology/approachIn response to the Covid-19 pandemic, an organization had modified its training procedures over the course of three years to improve employee and organizational outcomes. Employee candidates who were onboarded into sales positions during the years 2019–2021 were given the opportunity to learn the content in an online format and subsequently evaluated prior to their in-person training and final evaluation to provide them with a self-paced blended learning experience. Both evaluation scores, along with the length it took to complete the trainings, were used to determine the trainings effectiveness and efficiency respectively.FindingsThe findings for the study showed that the organization was successfully able to improve upon the efficiency of the training by reducing training length and the effectiveness by improving employee outcomes.Research limitations/implicationsThe study’s design was limited to the probation process, which resulted in issues drawing conclusions for employee outcomes that were relevant to their long-term organizational success. This emphasizes the importance of comprehensive investigations for future practical studies.Practical implicationsThe findings allow for the improvement of blended learning models within real-world organizational contexts that provide organizations with the opportunity to improve employee outcomes while reducing time costs.Originality/valueThis study provides data from applied blended learning procedures that were validated using empirical findings, which contributes to the practicality of blended learning in workplace training.

Return of the normal distribution: Flexible deep continual learning with variational auto-encoders

Learning continually from sequentially arriving data has been a long standing challenge in machine learning. An emergent body of deep learning literature suggests various solutions, through introduction of significant simplifications to the problem statement. As a consequence of a growing focus on particular tasks and their respective benchmark assumptions, these efforts are thus becoming increasingly tailored to specific settings. Whereas approaches that leverage Variational Bayesian techniques seem to provide a more general perspective of key continual learning mechanisms, they however entail their own caveats. Inspired by prior theoretical work on solving the prevalent mismatch between prior and aggregate posterior in deep generative models, we return to a generic variational auto-encoder based formulation and investigate its utility for continual learning. Specifically, we propose to adapt a two-stage training framework towards a context conditioned variant for continual learning, where we then formulate mechanisms to alleviate catastrophic forgetting through choices of generative rehearsal or well-motivated extraction of data exemplar subsets. Although the proposed generic two-stage variational auto-encoder is not tailored towards a particular task and allows for flexible amounts of supervision, we empirically demonstrate it to surpass task-tailored methods in both supervised classification, as well as unsupervised representation learning.

Generative Models of Brain Dynamics.

This review article gives a high-level overview of the approaches across different scales of organization and levels of abstraction. The studies covered in this paper include fundamental models in computational neuroscience, nonlinear dynamics, data-driven methods, as well as emergent practices. While not all of these models span the intersection of neuroscience, AI, and system dynamics, all of them do or can work in tandem as generative models, which, as we argue, provide superior properties for the analysis of neuroscientific data. We discuss the limitations and unique dynamical traits of brain data and the complementary need for hypothesis- and data-driven modeling. By way of conclusion, we present several hybrid generative models from recent literature in scientific machine learning, which can be efficiently deployed to yield interpretable models of neural dynamics.

Online instruction for a humanized learning experience: Techniques used by college instructors

The online learning literature converges to suggest that a “humanized” learning experience is critical to student engagement, where student agency, instructor presence, and peer presence have been nominated across a number of studies as key elements of a humanized online learning experience. Despite theoretical support, description of specific implementation techniques used by instructors to enhance these elements is limited. Based on an extensive review of the literature, this study developed a coding scheme of humanization practices and conducted content analysis of 244 excerpts of teaching practices from 17 online courses that demonstrated high levels of humanized instruction out of 100 randomly selected online courses at a large community college. The results reveal eight techniques that instructors used to humanize their online courses. These techniques indicate that to offer students a humanized learning experience, it is critical for instructors to make consistent and regular efforts throughout the course, promote social and academic presence in an integrated way, and proactively guide student social-emotional communication. These techniques, along with the concrete examples, lay a groundwork for future research about the relationship between humanization techniques and student outcomes in virtual environments and provide actionable guidance on how to humanize an online course.

Towards Proactive Surveillance through CCTV Cameras under Edge-Computing and Deep Learning

Weapons, usually a handgun, a revolver, or a pistol, are used in the majority of criminal acts. The traditional closed-circuit television (CCTV) surveillance and control system requires human intervention to detect such crime incidents. The purpose of this research is to develop a real-time automatic weapon carrier detection system that may be used with CCTV cameras and surveillance systems. The goal is to alarm and alert the security officials to take proactive action to prevent violent activities. In deep learning literature, region-based classifiers (R-FCN and Faster R-CNN) and regression-based detectors (Yolo invariant) are being used as promising object detection methods. Although region-based classifiers are accurate, they lack the speed of detection required for real-time detection, whereas regression-based detectors (for example, YoloV4 invariant) are fast enough for real-time detection, but lack accuracy. The method applied in this study relies on Yolov4 to quickly detect anomalies, followed by R-FCN to boost detection accuracy by filtering out any false positives. A weapon dataset comprising 4430 locally and internationally available weapon photos with a 70–30 split ratio is used to train and test the system, which is subsequently evaluated using a live surveillance camera system. This hybrid system achieved a 90% accuracy with a low false positive rate, as well as 94% precision, 86% recall, and 89% F1 score. Our results prove that the proposed hybrid system is useful for proactive real-time surveillance to alarm the existence of a suspicious weapon carrier in a surveillance area.

When proxy-driven learning is no better than random: The consequences of representational incompleteness.

Machine learning is widely used for personalisation, that is, to tune systems with the aim of adapting their behaviour to the responses of humans. This tuning relies on quantified features that capture the human actions, and also on objective functions—that is, proxies – that are intended to represent desirable outcomes. However, a learning system’s representation of the world can be incomplete or insufficiently rich, for example if users’ decisions are based on properties of which the system is unaware. Moreover, the incompleteness of proxies can be argued to be an intrinsic property of computational systems, as they are based on literal representations of human actions rather than on the human actions themselves; this problem is distinct from the usual aspects of bias that are examined in machine learning literature. We use mathematical analysis and simulations of a reinforcement-learning case study to demonstrate that incompleteness of representation can, first, lead to learning that is no better than random; and second, means that the learning system can be inherently unaware that it is failing. This result has implications for the limits and applications of machine learning systems in human domains.

Connecting organizational learning and open innovation research: an integrative framework and insights from case studies of strategic alliances

Purpose This study aims to combine open innovation (OI) and organizational learning (OL) literature in the context of strategic alliances. Design/methodology/approach The authors develop a conceptual framework that links OI to OL literatures. The authors empirically validate this framework through four representative cases of dyadic strategic alliances in which the allied partners have (mainly) adopted an inbound or outbound OI strategy and (mainly) used an experiential or experimental learning approach to access knowledge in the alliance. Findings The authors propose and validate a framework that links two well-known OI strategies (inbound and outbound) and two types of OL, namely, experiential and experimental learning. This, in turn, allows us to propose four different typologies of learning opportunities that could be pursued by alliance partners. Research limitations/implications The research offers a framework that links experiential and experimental learning to inbound and outbound OI strategies. However, the authors focused on inbound and outbound OI. The authors are aware that sometimes coupled innovation could take place in alliances. Practical implications This study proposes best practices for companies willing to open their innovation processes with alliance partners by bearing in mind the learning outcomes that may be exploited through the alliance. Originality/value This study highlights four alliance archetypes emerging from the nexus between OI strategies to OL methods.

Gradient-based sparse principal component analysis with extensions to online learning.

Sparse principal component analysis is an important technique for simultaneous dimensionality reduction and variable selection with high-dimensional data. In this work we combine the unique geometric structure of the sparse principal component analysis problem with recent advances in convex optimization to develop novel gradient-based sparse principal component analysis algorithms. These algorithms enjoy the same global convergence guarantee as the original alternating direction method of multipliers, and can be more efficiently implemented with the rich toolbox developed for gradient methods from the deep learning literature. Most notably, these gradient-based algorithms can be combined with stochastic gradient descent methods to produce efficient online sparse principal component analysis algorithms with provable numerical and statistical performance guarantees. The practical performance and usefulness of the new algorithms are demonstrated in various simulation studies. As an application, we show how the scalability and statistical accuracy of our method enable us to find interesting functional gene groups in high-dimensional RNA sequencing data.

Examining effects of instructional strategies on student engagement in blended online courses

AbstractBackgroundBlended online courses, which combine synchronous and asynchronous online activities, have expanded rapidly in higher education. How to enhance student engagement in such courses is unclear, although it is recognized that student engagement is malleable through instructional strategies.ObjectivesGiven the above, this study aims to examine the influence of categories of strategies on student engagement in blended online courses.MethodsA conceptual framework of instructional strategies indicated as fostering student engagement in the relevant literature was first presented, divided in eight categories (structure, pace, relevance, active, choice, relationships, explanations, guide). Then a research framework linking the categories of strategies to student engagement dimensions (emotional‐cognitive, social, behavioral) was built and tested in blended online courses. Data collected in various disciplines and university levels at four universities (n = 482) were examined using partial least squares structural equation modeling.Results and ConclusionsThe structural model examination confirmed the combined effects of categories of instructional strategies on student engagement in such courses in all disciplines. Particularly, this study revealed that 1) establishing trusting relationships, 2) demonstrating the relevance of activities, content, and resources, and 3) maintaining a sustained course pace significantly impacted student engagement in blended online courses in all disciplines.TakeawaysThis study draws upon the blended learning literature to bring together key instructional strategies that foster student engagement while highlighting empirical quantitative evidence of their effects on student engagement in blended online courses. Detailed measures of categories of instructional strategies and student engagement dimensions also provide reliable instruments for future research.

A Neural Architecture Search for Automated Multimodal Learning

The boom of artificial intelligence in the past decade is owed to the research and development of deep learning and moreover, that of accessible deep learning. But the goal of Artificial General Intelligence (AGI) cannot be achieved by having application-specific, parameter sensitive neural networks that need to be defined and tuned for every use case. General intelligence also involves understanding different types of data, rather than having dedicated models for each functionality. Thus both automating machine learning while also giving importance to generalizing over multiple modalities has great potential to help move AGI research forward.We propose a generalizable algorithm-Multimodal Neural Architecture Search (MNAS) which can work on multiple modalities and perform architecture search in order to create neural networks that enable classification on multiple types of data for multiclass outputs. The work automates the development of a fusion architecture by building upon existing literature of multimodal learning and neural architecture search. The controller network which predicts the architecture has been designed such that it works on a reward model where the reward is dependent on accuracies of individual networks corresponding to each modality involved. The work shows good results with accuracy comparable to both unimodal classification on same data and manually created multimodal architectures wherein the experiments are performed on multiclass classification problem of image and text modalities. It also uses a shared parameter search graph ensuring that the computational complexity is less compared to several other neural architecture search algorithms.