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The purpose of this paper is to empirically investigate the relationships among the implementation of LSC practices. Particularly, the authors examine the adoption level of 27 LSC practices, which were identified in a literature review, within 113 manufacturing companies from different sectors located in Southern Brazil.

Data collected were analyzed by means of multivariate techniques (partial correlation analysis) and two control variables were used: level of onshore suppliers and company’s experience on lean manufacturing implementation.

The results showed that the relationship among practices may not always be synergic, since there are pairs of practices that, when concurrently implemented they tend to conflict, hindering their benefits.

The use of partial correlations between pairs of LSC practices provides means to better understand specific associations, disregarding the implementation effect of the whole set of practices. The research also provides managers and practitioners arguments to better comprehend how practices interact with each other under specific contexts.

The purpose of this paper is to propose a novel approach of fuzzy importance‐performance analysis (FIPA) to replace conventional importance‐performance analysis (IPA) for determining critical service attributes those really need to improve for achieving superior customer satisfaction.

First, referring numerous studies, conventional IPA has some erroneous assumptions, the customer satisfaction of attribute performance has the characteristic of three‐factor theory and the novel approach which integrates natural logarithmic transformation and partial correlation analysis is feasible for acquiring the implicitly derived importance of attributes. Second, according the fact and nature of fuzziness in human perception, this study applies fuzzy set theory to revise conventional IPA. Finally, the FIPA is proposed and subsequently implemented in a Taiwanese hot spring hotel case study.

The implementation of FIPA shows the determined critical service attributes are almost completely different from those attributes acquired by conventional IPA. Hence, the application of conventional IPA may cause practitioners make incorrect decisions of improvement priorities for service attributes and direct unsuitable quality‐based marketing strategies.

The proposed FIPA which integrates fuzzy set theory, three‐factor theory, partial correlation analysis and natural logarithmic transformation avoids the erroneous assumptions of conventional IPA, considers the nature of fuzziness in human perception and includes the actual importance of service attributes. Therefore, the proposed FIPA can effectively assist business managers in determining critical service attributes to improve service quality or customer satisfaction and to achieve competitive advantage.

There has been much written about the effects of downsizing on the financial health and the valuation of companies that engage in this practice. But this literature is fragmented, focusing on various aspects of companies, various reasons for downsizing, and various financial and market outcome measures. The purpose of this paper is to try and address some of this fragmentation by comparing those companies that downsized in 2008, whether financially healthy or not, with those companies that did not downsize.

The impact of the downsizing event was assessed by using various financial measures as well as a measure of company valuation over the short term (2009-2011) and long term (2009-2014).

Findings indicate that across all financial measures, except return on equity, downsizing makes no difference to the financial health of a company either in the short term (up to three years after the downsizing) or in the long term (up to six years after the downsizing). And with regards to return on equity, downsizing companies did more poorly immediately after the downsizing in efficiently using their equity.

The hope is that this work will better inform, not only scholars, but also senior leaders faced with a decision to downsize or not to downsize.

The purpose of this paper is to establish correlations among the input variables of production within themselves and input variables of consumption within themselves and to forecast the production and consumption of the rice.

The production and consumption of rice crop is governed by diverse variables. In the present study five key input variables for production of rice based on literature review and the authenticated data available from agricultural sources have been selected. These variables are area sown, agricultural workers (AW), area irrigated, growth rate and yield per hectare. On similar basis four key input variables responsible for consumption of rice are considered, namely, price of rice, population, poverty ratio and per capita net national product (NNP).

Correlation analysis showed that priority wise production of rice depends upon yield per hectare, percentage irrigation, AW and area sown. The growth rate is found to be having insignificant correlation with other variables of production and hence was omitted from subsequent study. Correlation analysis also showed that priority wise consumption depends upon whole sale price per ton, population and the per capita NNP. The poverty ratio is found to be having insignificant correlation with other variables of consumption and hence was omitted from subsequent study. The outcomes of the correlation analysis are utilized for designing rule base for fuzzy inference system (FIS) to forecast the production and consumption of the rice. Subsequently Bayesian technique is used to forecast production and consumption and its results are compared with the results of fuzzy inference analysis.

There are many techniques used for forecasting purpose but FIS and Bayesian technique outperform others. In the present study, the authors therefore focussed on these two techniques. Bayesian technique takes into account the expert opinion at the current conditions whereas FIS uses previously designed rule base. Besides discussing the appropriateness of these two techniques for forecasting production and consumption of rice, their forecasting outcomes will help in logistical and operational planning of the resources at national level, farmers’ level and traders’ level.

The ecological environment of the Loess Plateau, China, is extremely fragile under the context of global warming. Over the past two decades, the vegetation of the Loess Plateau has undergone great changes. This paper aims to clarify the response mechanisms of vegetation to climate change, to provide support for the restoration and environmental treatment of vegetation on the Loess Plateau.

The Savitsky–Golay (S-G) filtering algorithm was used to reconstruct time series of moderate resolution imaging spectroradiometer (MODIS) 13A2 data. Combined with trend analysis and partial correlation analysis, the influence of climate change on the phenology and enhanced vegetation index (EVI) during the growing season was described.

The S-G filtering algorithm is suitable for EVI reconstruction of the Loess Plateau. The date of start of growing season was found to gradually later along the Southeast–Northwest direction, whereas the date of the end of the growing season showed the opposite pattern and the length of the growing season gradually shortened. Vegetation EVI values decreased gradually from Southeast to Northwest. Vegetation changed significantly and showed clear differentiation according to different topographic factors. Vegetation correlated positively with precipitation from April to July and with temperature from August to November.

This study provides technical support for ecological environmental assessment, restoration of regional vegetation coverage and environmental governance of the Loess Plateau over the past two decades. It also provides theoretical support for the prediction model of vegetation phenology changes based on remote sensing data.

This study aims to investigate and model the complex dynamics of innovation enablers in construction projects aiming to develop a framework identifying actions for clients to promote innovation.

Through a comprehensive literature review, a conceptual model was previously derived to explain the client-driven enablers in promoting innovation. This was tested using the data from 131 Australian construction projects. Statistical analysis was conducted on the data using factor analysis and correlation analysis to test the model, which was validated using the case study approach. The testing and validating aspects are explained in this paper.

The constructs of the recommended model are idea harnessing, relationship enhancement, incentivization and project team fitness.

The difficulty of analyzing the complex dynamics happening within projects in relation to innovation has been a barrier to progress research in this area. The introduction of this model would pave the way for researchers to explore this area with ease.

As revealed in the detailed literature review undertaken, this is the first time that a comprehensive study has been conducted to identify client-led innovation enablers for construction projects. The results would benefit industry practitioners to achieve enhanced project outcomes in construction projects through innovation.

The paper aims to present a new two stage local causal learning algorithm – HEISA. In the first stage, the algorithm discoveries the subset of features that better explains a target variable. During the second stage, computes the causal effect, using partial correlation, of each feature of the selected subset. Using this new algorithm, the study aims to identify the actions that lead a student succeed or failure in a course.

The paper presents a brief review of main concepts used in this study: Causal Learning and Causal effects. The paper also discusses the results of applying the algorithm in education data set. Data used in this study was extracted from the log of actions of a Learning Management System, Moodle. These actions represent the behavior of 229 engineering students that take Algorithm and Data Structure course offered in a blended model.

The algorithm proposed in the paper identifies that features with weak relevance to a target may become relevant when computing the direct effect.

The algorithm needs to be improved to automatically discard attributes that are under a specific threshold of direct effect. Researchers are also encouraged to test the proposed propositions further.

The algorithm presented in this paper can be used to identify the mostly relevant features given a classification task.

This paper computes the direct effect of a selected subset of features in a target variable to evaluate if a variable in this subset is really a cause of the target or if it is a spurious correlation.

The objective of this research is twofold. First, we aim at identifying the pairwise relationships between Lean Production (LP) practices and Industry 4.0 (I4.0) technologies. Second, based on these results, we propose a framework for Lean Automation (LA) implementation, in which I4.0 technologies are integrated into LP practices.

To achieve that, a cross-sector survey was performed with 147 manufacturers that are undergoing a LP implementation aided by novel information and communication technologies from I4.0. Multivariate data analysis was applied in order to underpin the proposed framework for LA.

Findings indicate that I4.0 technologies are positively correlated with LP practices, providing evidence to bear the proposition of a LA framework that can potentially overcome traditional barriers and challenges of a LP implementation.

As previous studies have approached LA implementation from a narrow perspective or including a limited set of LP practices and I4.0 technologies, the proposition of an integrated framework unfolds a wider range of synergistic implementations that may corroborate to a holistic approach for continuous improvement in the Fourth Industrial Revolution era.

This paper aims to investigate the statistical and geographical links between the prevalence of obesity and a range of socio‐economic indicators in a major UK city

The geographical pattern of fresh fruit and vegetable retailing was mapped across Birmingham (UK), and this data was combined with UK census data from Neighbourhood Statistics to investigate possible correlations between obesity and the social geography of this city. To further elicit the varying underlying links between obesity and social conditions, a methodology of partial correlations was used to create “social transects” across Birmingham so the operational effects of social conditions upon poor diet could be investigated across a range of Birmingham neighbourhood types.

Across Birmingham as a whole, people whose ethnic or social make‐up did not fit the dominant group in their neighbourhood were more likely to be obese than those of the majority socio‐ethnic group for that area. The level of qualifications was the dominant influencer on obesity and diet. Particularly, less wealthy people in the more affluent areas of Birmingham were likely to suffer financial difficulties in eating healthily. However, in less affluent areas, being in (low‐paid) work actually increased the chances of being obese, as compared to being unemployed in these districts.

Changes in the pattern of retailing or changes in individual's social status over the period of this research may confound the results; however the research may be regarded as a snapshot of conditions in Birmingham in ca.2006. The areal analysis may be confounded by the MAUP problem, although as distance to shops does not emerge as a major predictor of obesity, the results are still valid. The research applies to only one city (Birmingham), although a wide range of neighbourhood types typical of other British cities are covered.

Time limitations emerge as a significant factor in diet, especially in the less‐affluent areas of Birmingham. The significance of a range of social indicators upon diet is greatly affected by the range of neighbourhood types sampled. Factors barring access to a healthy diet can vary upon very small scales, even down to the individual household. Distance to shops has an effect upon diet, but only as a “moderating factor” acting in conjunction with a wider range of economic and social factors.

The effects of poverty, and especially unemployment, have very different effects upon diet and obesity in poor as compared to affluent areas; and in poorer areas, time limitations upon households operate so as to worsen the diet of those in low‐paid work. This implies that dietary improvement initiatives aimed at the less well off should aim for a compromise between health and convenience; otherwise such initiatives will merely widen health inequalities. Minority groups in all areas, whether a minority by ethnicity, age, or wealth, need special attention by dietary investigators.

The use of partial correlations to elicit the different responses to socio‐economic conditions as regards diet has not been applied before to a major UK city. The distance to shops for all residential areas for a major UK city has not been previously mapped.

This paper aims to explore the influence of wealth, transparency and democracy on the number of universities per million people ranked among the top 300 and 500. The highly ranked universities in the world tend to be concentrated in a few countries.

ANOVA was used to test the differences between the two groups in terms of three key national variables that are likely to affect higher education. These variables are gross domestic product per capita (GDPPC), transparency and democracy. Cluster analysis was used to find out if there were any groupings of countries based on the number of top-ranked universities and the above three variables. Correlation analysis was also used to confirm the relationship between the key variables and the number of universities among the top 500 and 300 universities per million people. Finally, partial correlations and linear regression were used to identify the most important factor(s) affecting the number of universities among the top 500 and 300 universities per million people.

ANOVA revealed that countries with top-ranked universities had higher GDPPC, transparency and democracy levels than countries with no top-ranked universities. Cluster analysis resulted in two groups of countries: one group of countries with a higher number of ranked universities, higher democracy level, higher transparency and higher GDPPC. The other group had lower values for each of the above variables. Pearson correlations confirmed that the number of top universities per million people is related to the three national variables. Partial correlation and regression revealed that transparency is the only significant factor that directly determines the number of universities among the top 500 in the world. On the other hand, both transparency and GDPPC are significant in determining the number of universities ranked in the top 300 per million people. This means that universities ranked among the top 500 are generally found in countries with higher transparency levels, while top 300 universities are generally found in countries that have both higher transparency levels and higher GDPPC.

This paper paves the way for further research on transparency and ethics in higher education. The impact of wealth, transparency and democracy can also be measured in other industries, and particularly those linked to learning and innovation.

This paper draws the attention to the fact that top universities are found in richer, more democratic and more transparent countries. It, however, indicates that the single factor associated with the number of universities ranked among the top 500 is transparency. Policy makers should therefore focus on increasing transparency to increase the likelihood of having top-ranked universities. Having universities ranked among the 300 is also linked to having higher GDPPC. This indicates that transparency can drive all other factors associated with having universities ranked among the first 500 in the world, but further improvement of university ranking to first 300 universities also requires greater financial strength. This is understandable given the expense associated with attracting first-class faculty members and having world-class research programs.

This is the first paper that researched the differences between countries having top-ranked universities and countries having no top-ranked universities. It also identified transparency as the single factor affecting the number of top 500 ranked universities per million people. It is also the first paper to have distinguished between having universities ranked among the top 300 in the world and universities ranked among the top 500. It showed that factors expected to be play a key role in determining higher education success such as democracy index and GDPPC were not significant in determining the number of top 500 universities per million people. This paper, however, showed that the number of top 300 universities per million per country depends not only on transparency but also on GDPPC. This paper provides a good understanding of barriers to university excellence in many countries. These barriers may also be pertinent to other institutions where learning is highly significant.