Knowledge production within the field of business research is accelerating at a tremendous speed while at the same time remaining fragmented and interdisciplinary. This makes it hard to keep up with ...state-of-the-art and to be at the forefront of research, as well as to assess the collective evidence in a particular area of business research. This is why the literature review as a research method is more relevant than ever. Traditional literature reviews often lack thoroughness and rigor and are conducted ad hoc, rather than following a specific methodology. Therefore, questions can be raised about the quality and trustworthiness of these types of reviews. This paper discusses literature review as a methodology for conducting research and offers an overview of different types of reviews, as well as some guidelines to how to both conduct and evaluate a literature review paper. It also discusses common pitfalls and how to get literature reviews published.
•A review on Machine and Deep Learning methods applied to industrial problems.•Papers from 2000 are analyzed and hierarchically catalogued.•Application domains, trend, and evolutions are ...investigated.
Machine Learning (ML) is a branch of artificial intelligence that studies algorithms able to learn autonomously, directly from the input data. Over the last decade, ML techniques have made a huge leap forward, as demonstrated by Deep Learning (DL) algorithms implemented by autonomous driving cars, or by electronic strategy games. Hence, researchers have started to consider ML also for applications within the industrial field, and many works indicate ML as one the main enablers to evolve a traditional manufacturing system up to the Industry 4.0 level. Nonetheless, industrial applications are still few and limited to a small cluster of international companies. This paper deals with these topics, intending to clarify the real potentialities, as well as potential flaws, of ML algorithms applied to operation management. A comprehensive review is presented and organized in a way that should facilitate the orientation of practitioners in this field. To this aim, papers from 2000 to date are categorized in terms of the applied algorithm and application domain, and a keyword analysis is also performed, to details the most promising topics in the field. What emerges is a consistent upward trend in the number of publications, with a spike of interest for unsupervised and especially deep learning techniques, which recorded a very high number of publications in the last five years. Concerning trends, along with consolidated research areas, recent topics that are growing in popularity were also discovered. Among these, the main ones are production planning and control and defect analysis, thus suggesting that in the years to come ML will become pervasive in many fields of operation management.
Stakeholder engagement has grown into a widely used yet often unclear construct in business and society research. The literature lacks a unified understanding of the essentials of stakeholder ...engagement, and the fragmented use of the stakeholder engagement construct challenges its development and legitimacy. The purpose of this article is to clarify the construct of stakeholder engagement to unfold the full potential of stakeholder engagement research. We conduct a literature review on 90 articles in leading academic journals focusing on stakeholder engagement in the business and society, management and strategy, and environmental management and environmental policy literatures. We present a descriptive analysis of stakeholder engagement research for a 15-year period, and we identify the moral, strategic, and pragmatic components of stakeholder engagement as well as its aims, activities, and impacts. Moreover, we offer an inclusive stakeholder engagement definition and provide a guide to organizing the research. Finally, we complement the current understanding with a largely overlooked dark side of stakeholder engagement. We conclude with future research avenues for stakeholder engagement research.
Organizations face continuous problems of survival and sustainability in the market, so innovation is vital for their growth. Entrepreneurship in the organization has been defined in various ways ...over the years, which has led to terminological confusion. Due to the innovation required by organizations for a proactive adaptation to the change and sustainability, intrapreneurship acquires special relevance for business development. Therefore, a literature review that considers intrapreneurship and the issues related to this concept is much needed. The search term ‘intrapreneur’ resulted in 312 articles published in WoS (Web of Science) between 1985 and 2021. These articles were analyzed using the VOSviewer software for the bibliometric analysis. The main authors and contributions in the area have been identified, in relation to the research objectives, enabling the generation of guidelines and proposals for future research.
Trimethylaminuria, better known as fish odor syndrome, is a psychologically disabling condition in which a patient emits a foul odor, which resembles that of rotting fish. The disorder is most ...commonly caused by an inherited deficiency in flavin monooxygenase 3, the vital enzyme for the metabolism of trimethylamine, which is the compound responsible for the unpleasant odor. The condition is uncommon, but there has been recent research to suggest that the diagnosis may often be overlooked. Moreover, it is important to be cognizant of this condition because there are reliable diagnostic tests and the disorder can be devastating from a psychosocial perspective. While there is no cure, many simple treatment options exist that may drastically improve the quality of life of these patients. This article will review the literature with an emphasis on the psychosocial impact and treatment options.
Learning analytics can improve learning practice by transforming the ways we support learning processes. This study is based on the analysis of 252 papers on learning analytics in higher education ...published between 2012 and 2018. The main research question is: What is the current scientific knowledge about the application of learning analytics in higher education? The focus is on research approaches, methods and the evidence for learning analytics. The evidence was examined in relation to four earlier validated propositions: whether learning analytics i) improve learning outcomes, ii) support learning and teaching, iii) are deployed widely, and iv) are used ethically. The results demonstrate that overall there is little evidence that shows improvements in students' learning outcomes (9%) as well as learning support and teaching (35%). Similarly, little evidence was found for the third (6%) and the forth (18%) proposition. Despite the fact that the identified potential for improving learner practice is high, we cannot currently see much transfer of the suggested potential into higher educational practice over the years. However, the analysis of the existing evidence for learning analytics indicates that there is a shift towards a deeper understanding of students’ learning experiences for the last years.
•Most learning analytics research undertake a descriptive approach.•Interpretative and experimental studies prevail.•Overall there is little evidence that shows improvements in learner practice.•The identified potential for improving learning support and teaching is high.•There is a shift towards a deeper understanding of students' learning experiences.
•Six debates on how organizations realize value from big data are identified.•Portability and interconnectivity are socio-technical features of big data.•Cross-level interactions influence big data ...value realization.•An integrated model of big data value realization is suggested.
Big data has been considered to be a breakthrough technological development over recent years. Notwithstanding, we have as yet limited understanding of how organizations translate its potential into actual social and economic value. We conduct an in-depth systematic review of IS literature on the topic and identify six debates central to how organizations realize value from big data, at different levels of analysis. Based on this review, we identify two socio-technical features of big data that influence value realization: portability and interconnectivity. We argue that, in practice, organizations need to continuously realign work practices, organizational models, and stakeholder interests in order to reap the benefits from big data. We synthesize the findings by means of an integrated model.
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