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The purpose of this paper is to determine the relationship between indoor environmental quality (IEQ) and occupants’ productivity improvements in green buildings in order to propose probable enhancements of national green certification criteria.

Survey approach was selected under quantitative phenomenon. Hence, questionnaire survey was selected as a primary data collection technique. It was linked to semi-structured interviews to validate survey results. The survey data were analysed by using nonparametric statistical analysis techniques, such as significance testing and Spearman’s correlation. Statistical Package for the Social Science (SPSS) v.20 statistical analysis software was used in data analysis.

Seven factors were identified as significant factors which showed statistically significant monotonic correlation to major IEQ dimensions. Among those, air quality and acoustical partitioning factors confirmed a statistically significant weak positive monotonic correlation, whilst system control showed strongly positive monotonic correlation to occupants’ productivity in green buildings.

The findings can be practically implied as a basis to review the existing evaluation criteria of IEQ in national green certification system to propose probable enhancements.

The significant IEQ factors influencing occupants’ productivity were determined as the focal point of this research. Accordingly, new provisions were proposed to enhance the national green certification criteria.

This article aims to close some research gaps by differentiating between brand types and price tiers. Many consumers perceive high prices as signals of high quality, yet researchers tend to find only low average correlations between price and objective quality. Previous studies do not account for market shares and paid prices though.

A German consumer panel with more than 30,000 households reveals market shares and paid prices. Combining these data with product test ratings, the authors evaluate price-quality relationships with Spearman’s rank correlation coefficients and distinguish food from non-food products, national brands and private labels and three price tiers.

High price-quality correlations for national brands and non-food private labels indicate that a higher price signals greater product quality. For food private labels, negative correlation coefficients inhibit the use of price as a quality indicator. The price-quality relationship for food private labels implies strong competition among brand owners, based on the price and quality of their products.

This article investigates price-quality correlations by accounting for paid prices and product market shares; it also reveals differences across food and non-food products, national brands and private labels and different price tiers against the background of competition strategies. By addressing when consumers use price as a quality indicator, it outlines important managerial implications for manufacturers, retailers and consumers.

Rolling bearings based on rotating machinery are one of the most widely used in industrial applications because of their low cost, high performance and robustness. The purpose of this paper is to describe how to identify degradation condition of rolling bearing and predict its fault time in big data environment in order to achieve zero downtime performance and preventive maintenance for the rolling bearing.

The degradation characteristic parameters of rolling bearings including intrinsic mode energy and failure frequency were, respectively, extracted from the pre-processed original vibration signals using EMD and Hilbert transform. Then, Spearman’s rank correlation coefficient and PCA were used to obtain the health index of the rolling bearing so as to detect the appearance of degradations. Furthermore, the degradation condition of the rolling bearings might be identified through implementing the monotonicity analysis, robustness analysis and degradation analysis of the health index.

The effectiveness of the proposed method is verified by a case study. The result shows that the proposed method can be applied to monitor the degradation condition of the rolling bearings in industrial application.

Further experiment remains to be done so as to validate the effectiveness of the proposed method using Apache Hadoop when massive sensor data are available.

The paper proposes a methodology for rolling bearing condition monitoring representing the steps that need to be followed. Real-time sensor data are utilized to find the degradation characteristics.

The result of the work presented in this paper form the basis for the software development and implementation of condition monitoring system for rolling bearings based on Hadoop.

Earned Value Management (EVM) is a project monitoring and control technique that enables the forecasting of a project's duration. Many EVM metrics and project duration forecasting methods have been proposed. However, very few studies have compared their accuracy and stability.

This paper presents an exhaustive stability and accuracy analysis of 27 deterministic EVM project duration forecasting methods. Stability is measured via Pearson's, Spearman's and Kendall's correlation coefficients while accuracy is measured by Mean Squared and Mean Absolute Percentage Errors. These parameters are determined at ten percentile intervals to track a given project's progress across 4,100 artificial project networks with varied topologies.

Findings support that stability and accuracy are inversely correlated for most forecasting methods, and also suggest that both significantly worsen as project networks become increasingly parallel. However, the AT + PD-ESmin forecasting method stands out as being the most accurate and reliable.

Implications of this study will allow construction project managers to resort to the simplest, most accurate and most stable EVM metrics when forecasting project duration. They will also be able to anticipate how the project topology (i.e., the network of activity predecessors) and the stage of project progress can condition their accuracy and stability.

Unlike previous research comparing EVM forecasting methods, this one includes all deterministic methods (classical and recent alike) and measures their performance in accordance with several parameters. Activity durations and costs are also modelled akin to those of construction projects.

The purpose of this paper is to develop a criterion for selection of critical sub‐processes when all the sub‐processes cannot be taken up simultaneously for improvement. There exist various methods but the practitioners get utterly confused because of the existence of these multiple options. In this paper, the goal is to assist practitioners in the selection of the critical sub‐processes.

The authors discuss various statistical methods such as correlation and regression, simulation, basic statistics such as average, standard deviation, coefficient of variation % (C.V.%), etc. for the selection and identification of the critical sub‐processes. The strengths and weaknesses of these methods have been compared through empirical analysis based on real‐life case examples.

The stepwise regression and simulation have been found to yield identical results. However, from the perspective of application, stepwise regression has been found to be a preferred option.

The roadmap thus evolved for the selection of the critical sub‐processes will be of great value to the practitioner, as it will help them understand the ground reality in an unambiguous manner, resulting in a superior strategy for process improvement.

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.

This article discusses the intra‐industry trade (IIT) aspect of international trade. The authors focus primarily on the trade between the United States and the 15 newly independent states of the former Soviet Union. Using data for these countries and the United States, this paper estimate the share of IIT of the 15 newly independent countries. The Pearson Product‐Moment Correlation and the Spearman's Rank Correlation Coefficients were employed to determine the relationship between the IIT share and the stages of economic development. The test of has been hypothesis was conducted to examine the significance of the relationship. As a result, the paper reveals that the IIT share is more closely related to GNP per capita than to the overall GNP, which suggests that GNP per capita may be a better measure of the stage of economic development.

This paper aims to investigate the association of COVID-19 confirmed cases and deaths with mental health, unemployment and financial markets-related search terms for the USA, the UK, India and worldwide using Google Trends.

The authors use Spearman’s rank correlation coefficients to assess the relationship between relative search volumes (RSVs) and mental health, unemployment and financial markets-related search terms, with the total confirmed COVID-19 cases as well as deaths in the USA, UK, India and worldwide. The sample period starts from the day 100 cases were reported for the first time, which is 7 March 2020, 13 March 2020, 23 March 2020 and 28 January 2020 for the US, the UK, India and worldwide, respectively, and ends on 25 June 2020.

The results indicate a significant increase in anxiety, depression and stress leading to sleeping disorders or insomnia, further deteriorating mental health. The RSVs of employment are negatively significant, implying that people are hesitant to search for new jobs due to being susceptible to exposure, imposed lockdown and social distancing measures and changing employment patterns. The RSVs for financial terms exhibit the varying associations of COVID-19 cases and deaths with the stock market, loans, rent, etc.

This study has implications for the policymakers, health experts and the government. The state governments must provide proper medical facilities and holistic care to the affected population. It may be noted that the findings of this study only lead us to conclude about the relationship between COVID-19 cases and deaths and Google Trends searches, and do not as such indicate the effect on actual behaviour.

To the best of the authors’ knowledge, this is the first attempt to investigate the relationship between the number of COVID-19 cases and deaths in the USA, UK and India and at the global level and RSVs for mental health-related, job-related and financial keywords.

Resource allocation has always been a critical problem with significant economic relevance. Many industries allocate the resources based on classical methods such as overall equipment effectiveness (OEE) and data envelopment analysis (DEA). The lack of OEE factors’ weight, how it is defined, analyzed, interpreted and compared in OEE and selection of unrealistic weights, self-appraisal and disability of complete ranking in DEA are challenges that are possible to occur. These defects may result in unfair allocation of the resources. This study aims to overcome the mentioned weaknesses.

In this paper, an approach using a set of various DEA models and Nash bargaining solution (NBS) is designed to solve the resource allocation problem based on OEE, among a set of comparable and uniform DMUs (decision-making units) in a fair way.

The results show that a unique Pareto optimal allocation solution is obtained by the proposed DEA–NBS model among the DMUs. This allocation is more acceptable for players, because the allocation results are commonly determined by all DMUs rather than a specific one. Furthermore, the rankings achieved by the utilized methods and TOPSIS (technique for order preference by similarity to ideal solution) are compared by Spearman’s rank correlation coefficient to validate the resource allocation plan. The findings indicate that the DEA–NBS method has the best correlation with the TOPSIS approach.

To the best of authors’ knowledge, no research has considered the use of DEA and NBS with OEE.

The application of conventional rank correlation tests to the comparison of relevance judgements is outlined and their disadvantages are noted. A weighted rank correlation coefficient rw is proposed, which assigns variable weights to exchanges of rank on different parts of the scale. Examples of its computation are given, both for tied and untied ranks, and the values of rw for n=3 and n=4 are tabulated. The chief mathematical properties of the coefficient are investigated.