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The purpose of this paper is to investigate the stochastic comparisons of the parallel system with independent heterogeneous Gumbel components and series and parallel systems with independent heterogeneous truncated Gumbel components in terms of various stochastic orderings.

The obtained results in this paper are obtained by using the vector majorization methods and results. First, the components of series and parallel systems are heterogeneous and having Gumbel or truncated Gumbel distributions. Second, multiple-outlier truncated Gumbel models are discussed for these systems. Then, the relationship between the systems having Gumbel components and Weibull components are considered. Finally, Monte Carlo simulations are performed to illustrate some obtained results.

The reversed hazard rate and likelihood ratio orderings are obtained for the parallel system of Gumbel components. Using these results, similar new results are derived for the series system of Weibull components. Stochastic comparisons for the series and parallel systems having truncated Gumbel components are established in terms of hazard rate, likelihood ratio and reversed hazard rate orderings. Some new results are also derived for the series and parallel systems of upper-truncated Weibull components.

To the best of our knowledge thus far, stochastic comparisons of series and parallel systems with Gumbel or truncated Gumble components have not been considered in the literature. Moreover, new results for Weibull and upper-truncated Weibull components are presented based on Gumbel case results.

Construction contract auctions are characterized by (1) a heavy emphasis on the lowest bid as it is that which usually determines the winner of the auction, (2) anticipated high outliers because of the presence of non‐competitive bids, (3) very small samples, and (4) uncertainty of the appropriate underlying density function model of the bids. This paper describes a method for simultaneously identifying outliers and density function by systematically identifying and removing candidate (high) outliers and examining the composite goodness‐of‐fit of the resulting reduced samples with censored normal and lognormal density function. The special importance of the lowest bid value in this context is utilized in the goodness‐of‐fit test by the probability of the lowest bid recorded for each auction as a lowest order statistic. Six different identification strategies are tested empirically by application, both independently and in pooled form, to eight sets of auction data gathered from around the world. The results indicate the most conservative identification strategy to be a multiple of the auction standard deviation assuming a lognormal composite density. Surprisingly, the normal density alternative was the second most conservative solution. The method is also used to evaluate some methods used in practice and to identify potential improvements.

The purpose of this paper is to provide empirical evidence on the relationship between capital structure and technical efficiency (TE) for Italian cereal farms during the 2008–2014 period. Emphasis is given in the understanding of the relationship between the level of financial leverage for cereal farms and their production performance.

The methods employed in this research article are based on non-parametric techniques in order to derive TE estimates for a sample of Italian cereal farms based on available Farm Accountancy Data Network data to explore in depth the relationship amongst the financial exposure of the sector and the capacity to utilise an efficient and effective production technology. Furthermore, subsidies are considered in the model as a non-discretionary variable and therefore, as an input that farmers cannot directly influence within the production function. Hence, the non-discretionary Data Envelopment Analysis model is a more appropriate framework since it is not penalising farms at a lower level of Pillar I payments when benchmarked with farms that receive a higher level of payments.

The results show that significant improvements could be achieved for most of the farms in the sample by improving production and management practices. Furthermore, results provide an empirical support of the adjustment theory by showing a negative impact of debt to asset ratio to TE.

This research article provides a first insight on the evolution of the Italian cereal farms debt-TE relationship in periods where high price instability has been observed.

Due to the large-size, non-uniform transactions per day, the money laundering detection (MLD) is a time-consuming and difficult process. The major purpose of the proposed auto-regressive (AR) outlier-based MLD (AROMLD) is to reduce the time consumption for handling large-sized non-uniform transactions.

The AR-based outlier design produces consistent asymptotic distributed results that enhance the demand-forecasting abilities. Besides, the inter-quartile range (IQR) formulations proposed in this paper support the detailed analysis of time-series data pairs.

The prediction of high-dimensionality and the difficulties in the relationship/difference between the data pairs makes the time-series mining as a complex task. The presence of domain invariance in time-series mining initiates the regressive formulation for outlier detection. The deep analysis of time-varying process and the demand of forecasting combine the AR and the IQR formulations for an effective outlier detection.

The present research focuses on the detection of an outlier in the previous financial transaction, by using the AR model. Prediction of the possibility of an outlier in future transactions remains a major issue.

The lack of prior segmentation of ML detection suffers from dimensionality. Besides, the absence of boundary to isolate the normal and suspicious transactions induces the limitations. The lack of deep analysis and the time consumption are overwhelmed by using the regression formulation.

In the context of online travel communities (OTCs), this paper aims to identify the relationships between value-creating practices, trusting beliefs, solution acceptance and stickiness and the moderating effects of risk aversion on the relationship between trusting beliefs and solution acceptance and on that between trusting beliefs and stickiness.

A total of 408 survey responses obtained from Amazon website panels were used to test the proposed hypotheses through a structural equation modeling analysis.

The results show that three dimensions of value-creating practices, namely, social networking, community engagement and brand use, had positive effects on trusting beliefs; trusting beliefs had positive effects on solution acceptance and stickiness; and solution acceptance had a positive effect on stickiness. Risk aversion moderated the trusting beliefs-stickiness relationship.

A key limitation of this study is related to the sample collected from Amazon website panels, which may limit the generalizability of results to other OTC members. The results have important theoretical and practical implications in OTC settings. For example, OTCs should be used by members as key platforms for acquiring trust information prior to their behaviors.

This study extends the OTC literature by integrating different research realms into the proposed research model for a better understanding of the relationships between value-creating practices, trusting beliefs and OTC behaviors.

This paper aims to investigate the moderating role of top management team (TMT) longevity on the TMT diversity‐firm performance relationship.

The paper presents results from a quantitative longitudinal study of 59 TMTs in German companies in different industries.

For age diversity, dominant educational background diversity, and diversity in dominant industry experience, the curvilinear moderating effect of TMT longevity on the TMT diversity–firm performance relationship is confirmed. However, for organizational tenure diversity, the form of the moderating effect is contrary to expectations (being u‐shaped).

In line with previous studies, the results were sensitive to the performance measures in use. Furthermore, the results should not be generalized since they may be sensitive to the sector under study and the small sample size.

First, a curvilinear moderating effect of TMT longevity on the TMT diversity‐firm performance relationship is investigated for the first time. Second, although the selected diversity dimensions have been investigated in previous TMT studies, they are examined simultaneously for the first time. Third, this study analyzes TMTs of large and medium‐sized German corporations operating in a variety of sectors. Fourth, relating demographic data on TMTs collected in 2004 to performance data for the years 2004 to 2007, the present paper presents one of the few longitudinal studies in the context of TMT diversity.

Data visualizations in some form or another have served as decision-support tools for many centuries. In conjunction with advancements in information technology, data visualizations have become more accessible and more efficient to generate. In fact, virtually all enterprise resource planning and human resource (HR) information system vendors offer off-the-shelf data visualizations as part of decision-support dashboards as well as stand-alone images and displays for reporting. Plus, advances in programing languages and software such as Tableau, Microsoft Power BI, R, and Python have expanded the possibilities of fully customized graphics. Despite the proliferation of data visualization, relatively little is known about how to design data visualizations for displaying different types of HR data to different user groups, for different purposes, and with the overarching goal of improving the ways in which users comprehend and interpret data visualizations for decision-making purposes. To understand the state of science and practice as they relate to HR data visualizations and data visualizations in general, we review the literature on data visualizations across disciplines and offer an organizing framework that emphasizes the roles data visualization characteristics (e.g., display type, features), user characteristics (e.g., experience, individual differences), tasks, and objectives (e.g., compare values) play in user comprehension, interpretation, and decision-making. Finally, we close by proposing future directions for science and practice.

The purpose of this paper is to proposes a forward search algorithm for detecting and identifying natural structures arising in human-computer interaction (HCI) and human physiological response (HPR) data.

The paper portrays aspects that are essential to modelling and precision in detection. The methods involves developed algorithm for detecting outliers in data to recognise natural patterns in incessant data such as HCI-HPR data. The detected categorical data are simultaneously labelled based on the data reliance on parametric rules to predictive models used in classification algorithms. Data were also simulated based on multivariate normal distribution method and used to compare and validate the original data.

Results shows that the forward search method provides robust features that are capable of repelling over-fitting in physiological and eye movement data.

One of the limitations of the robust forward search algorithm is that when the number of digits for residuals value is more than the expected size for stack flow, it normally yields an error caution; to counter this, the data sets are normally standardized by taking the logarithmic function of the model before running the algorithm.

The authors conducted some of the experiments at individual residence which may affect environmental constraints.

The novel approach to this method is the detection of outliers for data sets based on the Mahalanobis distances on HCI and HPR. And can also involve a large size of data with p possible parameters. The improvement made to the algorithm is application of more graphical display and rendering of the residual plot.

The purpose of this study is to examine the firms’ determinants of being acquired in Indian manufacturing sector. There is evidence of relationship between likelihood of being acquired and several firm specific characteristics such as age, size, research and development (R&D), advertising intensity, productivity, leverage, profitability, intangible assets and financial constraints. However, little is known about the association between these characteristics and likelihood of acquisition in Indian manufacturing sector.

The sample is a panel of 2,189 Indian manufacturing firms spanning almost 10 years (1998-2007). Random effects logistic (REL) regression model is adopted to control the firm specific unobserved heterogeneity in the sample. This is an essential requirement for providing accurate and effective determinants of being acquired.

Empirical results reveal that the determinants of being acquired in Indian manufacturing sector are age, size, R&D intensity, advertising intensity, productivity and leverage. The findings indicate that increase in firms’ age, size, R&D intensity and advertising intensity increases the likelihood of being acquired. However, increase in productivity and leverage decreases the likelihood of being acquired.

Findings of this study may be useful for potential targets to arrive at more thoughtful assessment of their attractiveness and, accordingly, promote their acquisition as a more efficient mode of exit.

The paper contributes some empirical evidence on the determinants of being acquired in Indian manufacturing sector by using panel data and REL regression model.