Resources in academic discourse: An empirical investigation of management journals

Commonly shared conceptualizations of resources are scant in academic management research which strikes as somewhat peculiar since resources and their allocation thereof have long been recognised to be at the heart of the competitive advantage and performance of a firm. The research literature considering resources as basis for competitive advantages has further faced contemporary criticism for the vagueness of the fundamental definition of the resource concept. Therefore, this paper empirically studies the representation of resource concept in academic management research literature. The paper reports results on the state of conceptualisations of organisations’ resources found in two distinct sources of research literature, namely ScienceDirect’s database and ISI’s top management journals, resulting in two data sets of a total of 457 articles. The findings illustrate the two-dimensional conceptual farrago in the conceptualisations; on the definitions of the resource concept itself and on the internal structure and the level of analysis when the concept is considered. In addition, the paper sheds light on the temporal evolution of the discourse explicitly considering resources. Finally, the paper considers several remedies for these deficiencies in order both to aid future theory development in management studies and to help increase the practical impact of the research in assisting managerial decision-makingPeer Reviewe

Total knee arthroplasty in a fast-track setting: reasons and risk factors for 90-day readmission

English abstrac

Experimental research setting in management

The current issue of the CERN IdeaSquare Journal of Experimental Innovation displays the plurality of levels of analysis for experimental research in management leaving only the policy level investigations untouched

Open laboratories for everyone wishing to experiment with the future

publishedVersionNon peer reviewe

Reply to Comment on Saku et al. : Reasons and risk factors for ninety day re-admission following primary total knee arthroplasty in a high-volume centre

Non peer reviewe

Structuration Mechanisms of a Creation Process of Local Energy Systems

Local energy systems (LESs) are becoming increasingly important vehicles for the energy sector in its quest for sustainable energy production, delivery and use. However, little is known about the creation processes of LES and microfoundations of these creation processes. Hence, in this exploratory case study, we investigate the mechanisms of LES creation. We build on multilevel approach to investigate micromacro mechanisms in this creation process. We explain how macro-level trends and decisions are translated into micro-level through actor properties and meso-level development of the LES. Furthermore, we describe how micro-level sensemaking leads to transformation mechanisms, including institutional work, marketing, and networking. Finally, we also suggest future research avenues based on our findings.acceptedVersionPeer reviewe

A Consultation Phone Service for Patients With Total Joint Arthroplasty May Reduce Unnecessary Emergency Department Visits

Background: Different measures for reducing costs after total joint arthroplasty (TJA) have gained attention lately. At our institution, a free-of-charge consultation phone service was initiated that targeted patients with TJA. This service aimed at reducing unnecessary emergency department (ED) visits and, thus, potentially improving the cost-effectiveness of TJAs. To our knowledge, a similar consultation service had not been described previously. We aimed at examining the rates and reasons for early postdischarge phone calls and evaluating the efficacy of this consultation service. Methods: During a 2-month period, we gathered information on every call received by the consultation phone service from patients with TJAs within 90 days of the index TJA procedure. Patients were followed for 2weeks aftermaking a call to detectmajor complications and self-initiated EDvisits. Datawere collected fromelectronic medical charts regarding age, gender, type of surgery, date of discharge, and length of hospital stay. Results: We analyzed 288 phone calls. Calls were mostly related to medication (41%), wound complications (17%), and mobilization issues (15%). Most calls were resolved in the phone consultation. Few patients (13%) required further evaluation in the ED. The consultation service failed to detect the need for an ED visit in 2 cases (0.7%) that required further care. Conclusion: The consultation phone service clearly benefitted patients with TJAs. The service reduced the number of unnecessary ED visits and functioned well in detecting patients who required further care. Most postoperative concernswere related to prescribed medications, wound complications, and mobilization issues. (c) 2017 Elsevier Inc. All rights reserved.Peer reviewe

Advancing Reproducibility and Accountability of Unsupervised Machine Learning in Text Mining : Importance of Transparency in Reporting Preprocessing and Algorithm Selection

Machine learning (ML) enables the analysis of large datasets for pattern discovery. ML methods and the standards for their use have recently attracted increasing attention in organizational research; recent accounts have raised awareness of the importance of transparent ML reporting practices, especially considering the influence of preprocessing and algorithm choice on analytical results. However, efforts made thus far to advance the quality of ML research have failed to consider the special methodological requirements of unsupervised machine learning (UML) separate from the more common supervised machine learning (SML). We confronted these issues by studying a common organizational research dataset of unstructured text and discovered interpretability and representativeness trade-offs between combinations of preprocessing and UML algorithm choices that jeopardize research reproducibility, accountability, and transparency. We highlight the need for contextual justifications to address such issues and offer principles for assessing the contextual suitability of UML choices in research settings.publishedVersionPeer reviewe

Advancing Reproducibility and Accountability of Unsupervised Machine Learning in Text Mining: Importance of Transparency in Reporting Preprocessing and Algorithm Selection

Machine learning (ML) enables the analysis of large datasets for pattern discovery. ML methods and the standards for their use have recently attracted increasing attention in organizational research; recent accounts have raised awareness of the importance of transparent ML reporting practices, especially considering the influence of preprocessing and algorithm choice on analytical results. However, efforts made thus far to advance the quality of ML research have failed to consider the special methodological requirements of unsupervised machine learning (UML) separate from the more common supervised machine learning (SML). We confronted these issues by studying a common organizational research dataset of unstructured text and discovered interpretability and representativeness trade-offs between combinations of preprocessing and UML algorithm choices that jeopardize research reproducibility, accountability, and transparency. We highlight the need for contextual justifications to address such issues and offer principles for assessing the contextual suitability of UML choices in research settings