Rivals’ Reactions to Mergers and Acquisitions

Mergers and acquisitions research has principally focused on attributes of the acquiring firm and post-acquisition outcomes. To extend our knowledge, we focus on external factors, in particular rival responses, and explore when and how rivals respond to their competitor’s acquisitions. Leveraging the awareness–motivation–capability framework, we predict and find evidence that a rival’s dependence on markets in common with the acquirer, resource similarity between rival and acquirer, and a rival’s organizational slack increase the volume and, in some cases, also the complexity of a rival’s competitive actions following an acquisition. Furthermore, the type of acquisition positively moderates some of these relationships. The results extend our understanding of the influence of mergers and acquisitions on competitive dynamics in the marketplace

Within-layer Diversity Reduces Generalization Gap

Neural networks are composed of multiple layers arranged in a hierarchical structure jointly trained with a gradient-based optimization, where the errors are back-propagated from the last layer back to the first one. At each optimization step, neurons at a given layer receive feedback from neurons belonging to higher layers of the hierarchy. In this paper, we propose to complement this traditional 'between-layer' feedback with additional 'within-layer' feedback to encourage diversity of the activations within the same layer. To this end, we measure the pairwise similarity between the outputs of the neurons and use it to model the layer's overall diversity. By penalizing similarities and promoting diversity, we encourage each neuron to learn a distinctive representation and, thus, to enrich the data representation learned within the layer and to increase the total capacity of the model. We theoretically study how the within-layer activation diversity affects the generalization performance of a neural network and prove that increasing the diversity of hidden activations reduces the estimation error. In addition to the theoretical guarantees, we present an empirical study on three datasets confirming that the proposed approach enhances the performance of state-of-the-art neural network models and decreases the generalization gap.Comment: 18 pages, 1 figure, 3 Table

Coopetition and innovation. Lessons from worker cooperatives in the Spanish machine tool industry

This is an electronic version of the accepted paper in Journal of Business & Industrial Marketing[EN] Purpose – This paper aims to investigate how the implementation of the inter-cooperation principle among Spanish machine-tool cooperatives helps them to coopete–collaborate with competitors, in their innovation and internationalization processes and achieve collaborative advantages. Design/methodology/approach – The paper uses a multi-case approach based on interviews with 15 CEOs and research and development (R&D) managers, representing 14 Spanish machine tool firms and institutions. Eight of these organizations are worker-cooperatives.. Findings – Worker -cooperatives achieve advantages on innovation and internationalization via inter-cooperation (shared R&D units, joint sales offices, joint after-sale services, knowledge exchange and relocation of key R&D technicians and managers). Several mutual bonds and ties among cooperatives help to overcome the risk of opportunistic behaviour and knowledge leakage associated to coopetition. The obtained results give some clues explaining to what extent and under which conditions coopetitive strategies of cooperatives are transferable to other types of ownership arrangements across sectors. Practical implications – Firms seeking cooperation with competitors in their R&D and internationalization processes can learn from the coopetitive arrangements analyzed in the paper. Social implications – Findings can be valuable for sectoral associations and public bodies trying to promote coopetition and alliances between competitors as a means to benefit from collaborative advantages. Originality/value – Focusing on an “ideal type” of co-operation -cooperative organisationsand having access to primary sources, the paper shows to what extent (and how) strong coopetitive structures and processes foster innovation and internationalization

Learning Task Relatedness in Multi-Task Learning for Images in Context

Multimedia applications often require concurrent solutions to multiple tasks. These tasks hold clues to each-others solutions, however as these relations can be complex this remains a rarely utilized property. When task relations are explicitly defined based on domain knowledge multi-task learning (MTL) offers such concurrent solutions, while exploiting relatedness between multiple tasks performed over the same dataset. In most cases however, this relatedness is not explicitly defined and the domain expert knowledge that defines it is not available. To address this issue, we introduce Selective Sharing, a method that learns the inter-task relatedness from secondary latent features while the model trains. Using this insight, we can automatically group tasks and allow them to share knowledge in a mutually beneficial way. We support our method with experiments on 5 datasets in classification, regression, and ranking tasks and compare to strong baselines and state-of-the-art approaches showing a consistent improvement in terms of accuracy and parameter counts. In addition, we perform an activation region analysis showing how Selective Sharing affects the learned representation.Comment: To appear in ICMR 2019 (Oral + Lightning Talk + Poster

Zero-Shot Cross-Lingual Transfer with Meta Learning

Learning what to share between tasks has been a topic of great importance recently, as strategic sharing of knowledge has been shown to improve downstream task performance. This is particularly important for multilingual applications, as most languages in the world are under-resourced. Here, we consider the setting of training models on multiple different languages at the same time, when little or no data is available for languages other than English. We show that this challenging setup can be approached using meta-learning, where, in addition to training a source language model, another model learns to select which training instances are the most beneficial to the first. We experiment using standard supervised, zero-shot cross-lingual, as well as few-shot cross-lingual settings for different natural language understanding tasks (natural language inference, question answering). Our extensive experimental setup demonstrates the consistent effectiveness of meta-learning for a total of 15 languages. We improve upon the state-of-the-art for zero-shot and few-shot NLI (on MultiNLI and XNLI) and QA (on the MLQA dataset). A comprehensive error analysis indicates that the correlation of typological features between languages can partly explain when parameter sharing learned via meta-learning is beneficial.Comment: Accepted as long paper in EMNLP2020 main conferenc

The Effect Of Intrateam And Interteam Trust On Organizational Outcomes: A Multilevel Study

The main objectives of this dissertation were to examine the main and interactive effects of intrateam and interteam trust on organizational outcomes at individual, team and organizational levels. Also, this dissertation sought to examine the mechanisms (team processes: team behavioral integration, team psychological safety, team reflexivity, and team learning) through which intrateam and interteam trust elicit organizational outcomes. Moreover, this dissertation also sought to uncover if value congruence and team feedback seeking behavior in teams moderate the effect of intrateam trust on the team processes. Hypotheses were tested using data collected from a sample of 282 team members nested under 78 teams and 23 branches from a major bank in Addis Ababa, Ethiopia, at two different time points. The results showed that intrateam trust has a significant effect on employees’ job satisfaction and job engagement at both individual and team levels. Interteam trust was also found to have a significant effect on individual and unit level performance. In addition, this dissertation also showed that team processes were important mediators of the effect of intrateam trust on organizational outcomes. Contrary to the hypotheses, however, the results showed that intrateam trust had no significant effect on performance at individual, team, and unit levels. Neither did team reflexivity, team behavioral integration, and team learning mediate the relationship between interteam trust and outcomes. Theoretical and practical implications are discussed