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As reimbursements fall and costs for services climb, organizations are forced to follow the painful motto of doing more with less. A solution could be the adaptation of industrial business process improvement (BPI) methods such as Lean to the hospital setting (HS). The purpose of this paper is to analyze if Lean approaches related in the literature provide sufficient methodological support for other practitioners to reproduce the reported results.

The authors analyzed the published Lean literature in the HS using a methodological maturity-level framework and what the authors defined as the 11 characteristic activities of BPI.

The literature analysis reveals that a Lean approach with a high-methodological maturity level that includes the 11 characteristic activities of BPI has never been reported. Considering this, the paper suggests a meta model for a high-methodological maturity-level Lean method based on the characteristic activities of BPI.

This is the first study on the Lean approach in the HS that evidences the absence of a robust Lean methodology in the literature. For Lean to be adopted and implemented by hospital practitioners a structured robust method should be provided.

The purpose of this paper is to propose a leagile transformation model for product development that guides manufacturers in the construction of a road map and the management of its deployment in line with both lean and agile improvement objectives.

An intervention qualitative and transformative research approach was adopted in order to develop required knowledge to theorise professional practice made from rigorous observations of facts. The research project took place over a period of two and a half years, in partnership with an international firm that develops and produces a wide range of luxury products.

The application of the methodology proved that a lean transformation does not have to be generated only by the field needs but it can follow a mixed approach where a top-down transformation management linked with strategic objectives is deployed without compromising implication and needs from people on the field. The right balance can be found between the strategic aspect of transformation and the incremental aspect on the field of lean paradigms.

For complete validation and widespread scientific application, the model should be tested in other sectors and industries.

The application case of the leagile model in several divisions of a luxury organisation proved that the proposed approach can be used as a guide for manufacturer in the construction of an improvement road map and in the management of its deployment. The application cases enabled a number of positive results to be generated and measured on quantitative indicators such as service ratio for new products for which, one of the divisions saw an increase of 30 per cent. The approach created a positive revolution among development team members by its potential in terms of communication, steering, benchmarking and knowledge system.

The model supports the identification and prioritisation of improvement initiatives by focusing on the levers for improvement that meet the needs and objectives of transformation, as well as the organisation’s maturity level.

The aim is to reveal contemporary research trends and patterns in Internet of Things (IoTs) so that social scientists who are new to the discipline may be steered towards rightful directions when examining this phenomenon.

A total of 169 IoT articles indexed in the Web of Science database were analyzed via bibliometric analysis and content analysis. The VOSViewer software was used to identify popular keywords of the IoT topics, its publication productivity, the most relevant journals, and the most prolific authors within. Content analysis was conducted manually to determine the most popular research methods used, the most frequently studied contexts, the most popular IoT application areas, the most highly examined user perspectives, and the most often employed theories.

The synthesis of both the bibliometric and content analysis results suggest the necessity of investigating the post-adoption technology usage behavior of IoT technology in developing countries, particularly in smart home. This is especially so from new landscapes using other theories or models, apart from the overwhelmed Technology Adoption Model (TAM) and its variants.

With a focus on addressing the state-of-the-art of IoT in social science, and to synthesize its future research directions systematically, this study was conducted with both bibliometric and content analysis, in order to enhance the overall analysis for higher accuracy and more reliable results.

In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research.

We propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice.

The literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples.

Based on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management.