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The scalability of similarity joins is threatened by the unexpected data characteristic of data skewness. This is a pervasive problem in scientific data. Due to skewness, the uneven distribution of attributes occurs, and it can cause a severe load imbalance problem. When database join operations are applied to these datasets, skewness occurs exponentially. All the algorithms developed to date for the implementation of database joins are highly skew sensitive. This paper presents a new approach for handling data-skewness in a character- based string similarity join using the MapReduce framework. In the literature, no such work exists to handle data skewness in character-based string similarity join, although work for set based string similarity joins exists. Proposed work has been divided into three stages, and every stage is further divided into mapper and reducer phases, which are dedicated to a specific task. The first stage is dedicated to finding the length of strings from a dataset. For valid candidate pair generation, MR-Pass Join framework has been suggested in the second stage. MRFA concepts are incorporated for string similarity join, which is named as “MRFA-SSJ” (MapReduce Frequency Adaptive – String Similarity Join) in the third stage which is further divided into four MapReduce phases. Hence, MRFA-SSJ has been proposed to handle skewness in the string similarity join. The experiments have been implemented on three different datasets namely: DBLP, Query log and a real dataset of IP addresses & Cookies by deploying Hadoop framework. The proposed algorithm has been compared with three known algorithms and it has been noticed that all these algorithms fail when data is highly skewed, whereas our proposed method handles highly skewed data without any problem. A set-up of the 15-node cluster has been used in this experiment, and we are following the Zipf distribution law for the analysis of skewness factor. Also, a comparison among existing and proposed techniques has been shown. Existing techniques survived till Zipf factor 0.5 whereas the proposed algorithm survives up to Zipf factor 1. Hence the proposed algorithm is skew insensitive and ensures scalability with a reasonable query processing time for string similarity database join. It also ensures the even distribution of attributes.

This paper aims to explore the impact of Sharīʿah-compliant stocks on other investor risk preferences beyond the risk aversion, namely, prudence and temperance.

This paper uses the non-parametric model data envelopment analysis with the shortage function as a measure of performance. The model uses three specifications considering skewness and kurtosis that describe according to expected utility theory, prudence and temperance.

Results show that first, efficient portfolios consist mainly of conventional stocks in the three-model specification. Second, the skewness improvement is achieved only when considering conventional stocks while Sharīʿah-compliant assets do not exhibit any impact on the third moment. Finally, diversification through both conventional and Sharīʿah-compliant stocks does not lead to kurtosis reduction. Sharīʿah-compliant stocks in efficient portfolios are sensitive to return and risk solely, and hence, prudence and temperance as related to skewness and kurtosis measures can be ignored in optimal portfolio selection during normal market conditions.

Findings suggest the same conclusions for four Islamic screening methods; however, readers should be prudent due to the limited sample. Results show that Sharīʿah-compliant assets do not have an impact on higher-order moments optimal portfolio returns, and hence, question the commonly admitted assumption of non-normality return distributions at least for Sharīʿah-compliant stocks.

The research findings suggest that Islamic investor preferences are described only by return and risk along with Sharīʿah criteria for stock selection and portfolio allocation. Portfolio managers should not care about higher-order moments to manage Sharīʿah-compliant funds. The traditional mean-variance Markowitz framework will be sufficient for investment or allocation decision-making. Description of Sharīʿah-compliant portfolio returns with only the first two order moments gives such asset more resiliency to extreme events like a crisis.

This research is the first in literature exploring whether prudence and temperance defined by higher-order moments can be drivers, besides Sharīʿah criteria, in portfolio allocation decision-making. This study is unique in terms of methodology and application. It uses individual stock data on the Casablanca Stock Exchange.

The purpose of this paper is twofold. Firstly to check the reliability between and within the parameters of total quality management–supply chain management (TQM–SCM) questionnaire and distribution of data collected through questionnaire from SCM and combined approach (TQM–SCM) companies. Secondly, to analyze the correlation connection among dependent and independent parameters of both SCM and combined approach in order to check and increase confidence in the data obtained before conducting actual research.

In the present study, 45 Indian manufacturing organizations have been extensively surveyed to check the reliability of the data and then ascertain the inter-relationships between various independent factors and competitive dimensions of SCM alone and for combined approach (TQM–SCM).

Firstly, the data for both the approaches are reliable. Secondly, independent variables (X1–X8) possess more strong correlation with business performance parameters of combined approach (TQM–SCM) companies as compared to only SCM companies.

TQM and SCM are considered as performance improvement techniques by the manufacturing organizations. As far as the author knows, this is the first study that is designed to find the interconnection between implementation factors as well as competitive dimensions of SCM approach and TQM–SCM approach in the context of Indian manufacturing organizations.

This study aims to explain the relationship of employee voice and turnover intention with the mediating role of top management team (TMT) conflicts. Moreover, this study also aims to find the moderating influence of union instrumentality among employee voice and TMT conflicts.

The study was conducted by using a quantitative approach and data was collected from 300 employees of the manufacturing sector of Pakistan through questionnaires. Data were analyzed by applying different statistical tools and tests through SPSS-21 and AMOS.

Results demonstrate that employee voice has a significant negative impact on employee turnover intention; TMT conflicts significantly mediate employee voice and intention to quit. However, union instrumentality is only initiated to moderate the relationship between employee voice and cognitive conflict.

This research focuses on the manufacturing sector and data have been collected from manufacturing firms situated in Gujranwala, Pakistan only. Moreover, the sample size of the study is also small. Therefore, the current study is an addition to the knowledge and understanding of the studied variables.

This study is of great use for managerial level employees because the adequate implication of employee voice can reduce turnover intention.

This study aims to add value to the existing exit-voice theory and discuss the internal organizational factors that generate quitting intentions. Moreover, it provides insights about union instrumentality and its significant role as a moderator and the significant mediating role between employee voice and intention to quit paving new ways for future researchers.

The occupational health risk associated with the production of prefabricated concrete components is often overlooked. This paper will use a damage assessment and cyclic mitigation (DACM) model to provide individualized exposure risk assessment and corresponding mitigation management measures for workers who are being exposed.

The DACM model is proposed based on the concept of life cycle assessment (LCA). The model uses Monte-Carlo simulation for uncertainty risk assessment, followed by quantitative damage assessment using disability-adjusted life year (DALY). Lastly, sensitivity analysis is used to identify the parameters with the greatest impact on health risks.

The results show that the dust concentration is centered around the mean, and the fitting results are close to normal distribution, so the mean value can be used to carry out the calculation of risk. However, calculations using the DACM model revealed that there are still some work areas at risk. DALY damage is most severe in concrete production area. Meanwhile, the inhalation rate (IR), exposure duration (ED), exposure frequency (EF) and average exposure time (AT) showed greater impacts based on the sensitivity analysis.

Based on the comparison, the DACM model can determine that the potential occupational health risk of prefabricated concrete component (PC) factory and the risk is less than that of on-site construction. It synthesizes field research and simulation to form the entire assessment process into a case-base system with the depth of the cycle, which allows the model to be continuously adjusted to reduce the occupational health damage caused by production pollution exposure.

Normalization is a crucial step in all decision models, to produce comparable and dimensionless data from heterogeneous data. As such, various normalization techniques are available but their performance depends on a number of characteristics of the problem at hand. Thus, this study aims to introduce a recommendation framework for supporting users to select data normalization techniques that better fit the requirements in different application scenarios, based on multi-criteria decision methods.

Following the proposed approach, the authors compare six well-known normalization techniques applied to a case study of selecting suppliers in collaborative networks.

With this recommendation framework, the authors expect to contribute to improving the normalization of criteria in the evaluation and selection of suppliers and business partners in dynamic networked collaborative systems.

This is the first study about comparing normalization techniques for selecting the best normalization in dynamic multiple-criteria decision-making models in collaborative networks.

The purpose of this paper is to examine the influence of innovation indicators: Internet usage, patent rights, innovation in exporting countries and innovation in the importing country on the export competitiveness of firms in the East African Community (EAC).

The study adopted the structural gravity model and the Poisson Pseudo Maximum Likelihood a nonlinear estimation method that was applied in STATA on balanced panel data from 2007 to 2018. Data were obtained from World Bank International Trade Center and World Bank development indicators.

Results show that innovation in the importing country, innovation in the exporting country and patent rights of exports are positive and significant predictors of export competitiveness in developing countries. While Internet usage is an insignificant predictor in the EAC.

There is a need to examine the complicated nature of the EAC economy to further this study's findings.

Exporting countries need to take deeper reforms as regards structural transformation to enable firms to integrate into the Global Value Chains (GVCs) to enable them to increase their productivity by reviewing the existing policies to match the changes in the market.

This study explains the complex dynamic interactions of technological innovation indicators in the EAC using quantitative data and that this interaction has an effect on the export competitiveness in import-oriented countries with less harmonization in their trade policies.

The purpose of this paper is to examine the influence of export logistics components: shipment arrangements, timely delivery, customs quality, trade infrastructure, and tracking and tracing on export competitiveness of firms in the East African Community (EAC).

The study adopted the Structural Gravity Model and the Poisson pseudo-maximum likelihood (PPML). PPML a nonlinear estimation method was applied in STATA on a balanced panel data for the period of 2007–2018. Data were obtained from World Bank International Trade Centre (ITC), World Bank Logistics Performance Index (LPI) and World Bank development indicators.

Results show that timely delivery and tracking and tracing of exports are positive and significant predictors of export competitiveness in EAC countries. Conversely, shipment arrangements, customs quality and trade infrastructure have no influence on export competitiveness.

The results of this study show that export logistics components of shipment arrangements, customs quality and trade infrastructure do not matter at the present in improving export competitiveness in the EAC. There is a need to examine the intricate nature of the EAC economy to further this study's findings.

The EAC partner states should embrace deep integration by removing the behind the border trade barriers in addition to other trade restrictions, to create a common economic space among member states. This will further shrink the delivery time and the tracking and tracing of exports hence improving the competitiveness of EAC exports within the region and outside. Also, common and harmonized trade policies and regulations should be implemented through mutual recognition agreements where countries agree to recognize one another's conformity assessments.

This study explains the complex dynamic interactions of export logistics factors in the EAC using quantitative data and that this interaction has an effect on the export competitiveness in import-dominated countries with less harmonization in their trade policies.

The purpose of this paper is threefold; first, to examine the Australian and Indian managerial attitudes towards social responsibility (SR) and assess their support for SR; second, to explore the underlying factors that shape beliefs and attitudes; and third, to explore whether respondents from these countries, characterised by differing levels of development, differ in their attitudes towards SR.

It investigates attitudes and support for SR of 318 Australian and Indian managers drawn from three industries. Eighteen social issues, principal component analysis (PCA) and confirmatory factor analysis (CFA) procedures were undertaken to explore and confirm the underlying factors of SR. The paper used legitimacy theory as theoretical framework.

An overall positive attitude towards SR is shown by the both groups. Indian respondents are concerned about a greater range of social issues than Australian respondents. Australian participants strongly supported a few issues surrounding SR, whereas Indian respondents strongly supported these and other issues. Significant (at 0.00 level) differences do exist between the two groups of managers on attitudes towards various social issues.

The questions used in the survey do not represent the entire framework on which attitudes towards SR are formed. Moreover, culture was not explicitly explored as a possible factor in the study.

The study provides a comparative analysis of the SR from the developed and developing economy perspective. Using organisational legitimacy theory the study analyse managerial attitudes with regard to maintaining pragmatic and moral legitimacy.

The purpose of this study is to examine the moderating effect of an exogenous corporate governance shock that curbs Chief Executive Officers’ (CEOs) power on the relationship between CEO narcissism and earnings management practices.

The authors performed a quasi-experiment using a differences-in-differences approach to examine Brazil’s duality split regulatory change on 101 Brazilian public firms during the period 2010–2022.

The main findings indicate that the introduction of duality split curtails the positive influence of CEO narcissism on earnings management, suggesting that this corporate governance regulation may act as a complementary corporate governance mechanism in mitigating the negative consequences of powerful narcissistic CEOs. Further robustness checks indicate that the results remain consistent after using entropy balancing and alternative measures of CEO narcissism.

In emerging markets, where governance systems are frequently perceived as less than optimal, policymakers and regulatory authorities can draw insights from this enforcement to shape governance systems, reducing CEO power and, consequently, improving the quality of financial reporting.

To the best of the authors’ knowledge, this is the first study to examine whether a duality split mitigates the influence of CEO narcissism on earnings management. Thus, this study contributes to the corporate governance literature that calls for research on the effectiveness of external corporate governance mechanisms in emerging markets as well as the CEO narcissism literature that calls for research on moderating factors that could curtail negative consequences of narcissistic CEO behavior.