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The purpose of this study is to examine the critical failure factors (CFFs) linked to various types of process improvement (PI) projects such as Kaizen, Lean, Six Sigma, Lean Six Sigma and Agile. Proposing a mitigation framework accordingly is also an aim of this study.

This research undertakes a systematic literature review of 49 papers that were relevant to the scope of the study and that were published in four prominent databases, including Google Scholar, Scopus, Web of Science and EBSCO.

Further analysis identifies 39 factors that contribute to the failure of PI projects. Among these factors, significant emphasis is placed on issues such as “resistance to cultural change,” “insufficient support from top management,” “inadequate training and education,” “poor communication” and “lack of resources,” as primary causes of PI project failures. To address and overcome the PI project failures, the authors propose a framework for failure mitigation based on change management models. The authors present future research directions that aim to enhance both the theoretical understanding and practical aspects of PI project failures.

Through this study, researchers and project managers can benefit from well-structured guidelines and invaluable insights that will help them identify and address potential failures, leading to successful implementation and sustainable improvements within organizations.

To the best of the author’s knowledge, this paper is the first study of its kind to examine the CFFs of five PI methodologies and introduces a novel approach derived from change management theory as a solution to minimize the risk associated with PI failure.

The purpose of this paper is to assess the extent of climate change likely to be manifested in the MENA region using statistical tools as well as outputs from physics‐based General Circulation Models (GCMs).

Atmospheric temperature and precipitation primarily capture climate change features and are considered the drivers of other manifestations of climate change such as rises in sea‐level, tropical cyclone intensities, severe floods, prolonged droughts, and retreating ice. Data on atmospheric temperature and precipitation have been statistically analysed for trend, distribution and variability in this study. Long‐range prediction is then made using time series analysis. Long‐range projections have also been made by many investigators using physics‐based GCMs and the Fourth Assessment Report of IPCC provides a summary. IPCC projections are not indisputable because of some inherent limitations of GCMs. A comparative study is made between statistical predictions and IPCC projections, as well as forecasts from some GCMs specifically applied to the region, to develop a more reliable forecast scenario. Water resources projects are quite vulnerable to changes in atmospheric temperature and precipitation amounts. The various aspects of planning, design and management of water resources projects which are likely to be influenced by climate change are discussed.

There is considerable variability in atmospheric temperature and precipitation in recent observations but if the variability is filtered out and the underlying trend extrapolated it is found that there is in general an agreement between IPCC projections and statistical predictions. For rise in atmospheric temperature projections made from many GCMs applied to the region, as well as projections summarised in the Fourth Assessment Report of IPCC, appear to be good estimates to be included in design considerations. For precipitation, statistical predictions are perhaps a better choice because GCM projections are less reliable with precipitation since associated meteorological processes occur at a much smaller scale than the grid size of a GCM. For low‐lying coastal regions sea‐level rise and more frequent extreme climatic events such as tropical cyclones add to the dimensionality of design considerations especially for infrastructure design.

This paper presents a comparative study of possible climate change in the long‐term between physics‐based model projections and statistical predictions. This should provide greater insight into climate change that is expected in MENA and reduce uncertainty, thereby instilling greater confidence in water resources planners and practitioners to incorporate climate change aspects into decision making. This research is believed to be particularly helpful because of scant research work done on this part of the globe on climate change.

Traditionally, the main goal of empirical research has been to test theories. Yet, theory-testing is problematical in the social sciences. Findings from empirical studies have proven hard to replicate and there is a lack of progress in creating a coherent and cumulative knowledge base. There are both practical and epistemological issues that prevent effective empirical tests. It is difficult to operationalize constructs and design decisive tests of theories. The laws and regularities posited in theories in the natural sciences are independent of human actors, while theories in the social sciences describe systems and structures that are created and maintained by human actors. Nonetheless, human actors are sometimes guided by theories. They may change their behavior or make different decisions based on academically produced knowledge. This relationship is usually mediated by the use of tools of various sorts (i.e., design principles, diagrams, or stories). I discuss why scholars should conduct empirical research to test the pragmatic validity of tools that are derived from theories rather than testing the scientific validity of the theories themselves.

To define synthetic indices of changes of vegetation coverage on salt marshes of the Venice lagoon that might be used to model changes of the entire basin.

Remote sensing data from the satellite sensor QuickBird were processed to retrieve vegetation coverage in different seasons, i.e. different phenological stages of the halophytic vegetation. These changes have been described by means of landscape metrics (LSM's) and statistical moments.

LSM's selected have been useful to describe the dominant shapes of vegetation coverage, while the low variations occurred were only partially explained. The first four statistical moments have been only partially functional to the description of temporal variation of the patches.

These indicators, whose performances have been evaluated by the present work, could be used to detect larger variations on longer period and on wider spatial scale. In this way it could also be possible to easily detect the effects of changes on the hydrodynamic regime that may occur in relation with new projected interventions on the Venice lagoon.

This approach has never been applied to salt marshes of the Venice lagoon. The remote sensing data, in particular LSM's and statistical moments applied together, might be valuable tools to detect shape changes in the delicate environment of salt marshes and suggest possible remedies for problems of managing the Venice lagoon.

This study seeks to consider the recent evolution of the Italian policy in the field of marine protected areas (MPAs). In particular, it aims to point out the increasing role played by Italian regions in establishing and managing MPAs. This evolution is to be described by focusing attention on the North Adriatic case.

After a brief introduction on the legislative framework, points of weakness and strength of the Italian experience in the field of MPAs are discussed. Different initiatives for promoting and implementing an MPA strategy are analyzed through the North Adriatic case study, pointing out the increasing role played by the regions in the design and implementation of protection policies.

The analysis shows how regions direct greater attention to the involvement of the public and economic sector in the design and implementation of protection policies, and to the definition of more flexible management tools. This element clearly confirms the general and overall tendency towards more regional‐based forms of environmental policy, even in coastal and marine affairs. However, in order to improve the environmental gains and implement sustainable forms of economic development, these regional initiatives should be better linked to a more comprehensive coastal zone management framework, which is still lacking. From this perspective, a national strategy for the Italian coast system could strongly contribute to increasing the role and effectiveness of regional initiatives aimed at protecting coastal and marine ecosystems.

The North Adriatic case shows the growing need for integrated policies in different scale projects, enhancing the involvement of local communities toward their own territory.

The purpose of this paper is aimed at understanding and identifying the context, capability requirements, and configurations (3Cs) of modular supply networks in the Chinese automotive industry.

In an attempt to specify the main elements of modular supply networks, a case study and 3C framework approach analyze different network contexts. The framework considers not only the capabilities of product design, production, inbound logistics, and information sharing, but also the configuration of the role structure, process structure, and information structure that helps achieve these capabilities.

This paper analyzes two types of modular supply network, directly reflecting the degrees of involvement in the modularity logic of the module supplier and the automaker, which largely depend on their corporate strategy toward technology innovation. In a fully integrated module supply network, the module supplier needs sufficient capability for upstream supply network integration and modularization, whereas in the partly integrated module supply network, a third‐party logistics provider plays an important role with regard to production capability.

This paper investigates four modules. The practical tools for configuring the modular supply network can be specified in more detail in further research.

The 3C framework described herein contributes to theory in the fields of supply chain management and modularity, and it assists in further expanding the theory of SCM.

The overall objective of this study is envisaged to provide decision makers with actionable insights and access to multi-risk maps for the most in-danger stave churches (SCs) among the existing 28 churches at high spatial resolution to better understand, reduce and mitigate single- and multi-risk. In addition, the present contribution aims to provide decision makers with some information to face the exacerbation of the risk caused by the expected climate change.

Material and data collection started with the consultation of the available literature related to: (1) SCs' conservation status, (2) available methodologies suitable in multi-hazard approach and (3) vulnerability leading indicators to consider when dealing with the impact of natural hazards specifically on immovable cultural heritage.

The paper contributes to a better understanding of place-based vulnerability with local mapping dimension also considering future threats posed by climate change. The results highlight the danger at which the SCs of Røldal, in case of floods, and of Ringebu, Torpo and Øye, in case of landslide, may face and stress the urgency of increasing awareness and preparedness on these potential hazards.

The contribution for the first time aims to homogeneously collect and report all together existing spread information on architectural features, conservation status and geographical attributes for the whole group of SCs by accompanying this information with as much as possible complete 2D sections collection from existing drawings and novel 3D drawn sketches created for this contribution. Then the paper contributes to a better understanding of place-based vulnerability with local mapping dimension also considering future threats posed by climate change. Then it highlights the danger of floods and landslides at which the 28 SCs are subjected. Finally it reports how these risks will change under the ongoing impact of climate change.

The selection of wavelength region and number of bands is a research problem for remote sensing experts for utilization of data provided by the sensor system. The present study proposes to make an evaluation for optimum band selection and classification accuracy.

The entropy, brightness value overlap index (BVOI), optimum index factor (OIF) and spectral separability analysis, i.e. Euclidean distance (ED), divergence, transformed divergence (TD) and Jefferies‐Matusita (JM) distance and accuracy of MLC classification were carried out. For the present study Terra ASTER, Landsat ETM⁺ and IRS 1D LISS III dataset has been used. The first three methods were for the spectral evaluation of the three satellite data used and for determination of information content, variance and spectral overlap among the classes present in the natural and man‐made landscape. The fourth method is for selection of spectral band combinations with highest separability of classes using divergence matrices. These band combinations are selected for the classification and subsequent accuracy assessment.

The OIF values are clearly indicating that the performance of ASTER data is the best, having the lowest correlation between the bands; hence the separability of the feature is also highest, while LISS III have shown high correlation between the bands, with the poor separability of the features. Landsat ETM⁺ data are in between these two sensors, better than LISS III but poorer than ASTER. The BVOI outputs of the three datasets of man‐made landscape show that band 3 of ASTER has the least overlap of the classes, followed by band 4 of ETM⁺. Very high overlap of the classes has been found in LISS III data. It has been found from spectral separability analysis of all the three datasets for the man‐made landscape that ASTER data with band combination of spectral bands 123468 contains the highest value of all the measures of spectral separability, i.e. ED (291.72), divergence (2,133.37), TD (2,000.00) and JM distance (1,414.10).

It can be inferred from the present study that spectral resolution plays a very important role in discrimination of vegetation features. ASTER data which are with the highest number of the bands amongst the satellite data used had shown highest classification accuracy, while LISS III data with lowest number of bands had shown lowest accuracy, and Landsat ETM⁺ stood in between the two sensors.

It is important to evaluate the sensor systems and their spectral regions for discrimination of vegetation features. The number of bands present in a particular sensor and the spectral regions used in it are some of the crucial factors which decide the usefulness of the data for different applications, including vegetation‐related studies. The selection of spectral wavelength region, i.e. spectral bands and the sensor system, presents the research problem for remote sensing experts to suggest the best spectral regions and satellite sensor for the discrimination of the vegetation features in different landscapes, namely man‐made and natural.

In the present study all the three datasets are extensively examined and tested for their vegetation discrimination capabilities using well‐established methodologies. All the parameters applied on the datasets revealed that spectral resolution definitely plays a role in the performance of the data as far as discrimination of features is concerned both in natural and man‐made landscape with desirable accuracy.

Self-regulation skills, such as organisation, self-evaluation, personal elaboration, metacognitive attitude and strategic awareness are very important predictors of academic achievement. However, research has not studied in depth the factors that facilitate the use of good self-regulatory skills. The present research was intended to study the role of some factors that could affect these self-regulation skills, in particular depressive attitude and motivational beliefs. A group of 246 adolescents, aged between 14 and 18, were administered self-report questionnaires devised to test aspects underlying self-regulation. A preliminary factor analysis confirmed the centrality of the three-hypothesised aspects: motivational beliefs, depressive attitude, and self-regulation-skills. A path analysis revealed that there are important links between motivational beliefs and self-regulation and between depressive attitude and motivational beliefs. Some educational implications are discussed.

Why are some lean workfloor teams able to improve their already high performance, over time, and others not? By studying teams' and leaders' behaviour-value patterns, this abductive field study uncovers a dynamic capability at the team level.

Various methods were employed over three consecutive years to thoroughly examine five initially high-performing lean workfloor teams, including their leaders. These methods encompassed micro-behavioural coding of 59 h of film footage, surveys, individual and group interviews, participant observation and archival data, involving objective and perceptual team-performance indicators. Two of the five teams continued to improve and perform highly.

Continuously improving high lean team performance is found to be associated with (1) team behaviours such as frequent performance monitoring, information sharing, peer support and process improvement; (2) team leaders who balance, over time, task- and relations-oriented behaviours; (3) higher-level leaders who keep offering the team face-to-face support, strategic clarity and tangible resources; (4) these three actors' endorsement of self-transcendence and openness-to-change work values and alignment, over time, with their behaviours; and (5) coactive vicarious learning-by-doing as a “stable collective activity pattern” among team, team leader, and higher-level leadership.

Since lean has been undertheorised, the authors invoked insights from organisational behaviour and management theories, in combination with various fine- and coarse-grained data, over time. The authors uncovered actors' behaviour-value patterns and a collective learning-by-doing pattern that may explain continuous lean team performance improvement. Four theory-enriching propositions were developed and visualised in a refined model which may already benefit lean practitioners.