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The purpose of this paper is to first develop indicators for how total inequality, measured through the ANalysis Of GIni (ANOGI) framework, is mapped onto each group – i.e. indicators for each group's share of total inequality. Second, to develop indicators for the sensitivity of total inequality and its structure to changes in the composition of the population. Specifically, to develop indicators for how the Gini index and its ANOGI components react to (1) changes in the population-share of each group, (2) migration between groups, (3) changes in group incomes and (4) income transfers between groups.

First, the expressions for these indicators are derived analytically. Following this, the indicators are applied to labour-market data from Brazil, contrasting the results to others available in the literature.

The indicators described above are presented and their characteristics discussed. Empirically, it is illustrated how labour formalisation in Brazil was an inequality-reducing process between 2002 and 2011, contrary to previous incorrect measurements of the phenomenon based on income-source decompositions for Latin American countries.

Indicators for how total inequality reacts to changes in group sizes and income were unavailable for the ANOGI framework, which this article provides. The empirical illustration shows how this leads to a reassessment of important inequality dynamics, using the example of labour formalisation in Brazil. Contrary to the existing literature, it is shown how this was a progressive development, with key implications for social and labour-market policy. This framework can be used to assess the impact of diverse processes in the ANOGI methodology.

Purchase conversion prediction aims to improve user experience and convert visitors into real buyers to drive sales of firms; however, the total conversion rate is low, especially for e-retailers. To date, little is known about how e-retailers can scientifically detect users' intents within a purchase conversion funnel during their ongoing sessions and strategically optimize real-time marketing tactics corresponding to dynamic intent states. This study mainly aims to detect a real-time state of the conversion funnel based on graph theory, which refers to a five-class classification problem in the overt real-time choice decisions (RTCDs)—click, tag-to-wishlist, add-to-cart, remove-from-cart and purchase—during an ongoing session.

The authors propose a novel graph-theoretic framework to detect different states of the conversion funnel by identifying a user's unobserved mindset revealed from their navigation process graph, namely clickstream graph. First, the raw clickstream data are identified into individual sessions based on a 30-min time-out heuristic approach. Then, the authors convert each session into a sequence of temporal item-level clickstream graphs and conduct a temporal graph feature engineering according to the basic, single-, dyadic- and triadic-node and global characteristics. Furthermore, the synthetic minority oversampling technique is adopted to address with the problem of classifying imbalanced data. Finally, the authors train and test the proposed approach with several popular artificial intelligence algorithms.

The graph-theoretic approach validates that users' latent intent states within the conversion funnel can be interpreted as time-varying natures of their online graph footprints. In particular, the experimental results indicate that the graph-theoretic feature-oriented models achieve a substantial improvement of over 27% in line with the macro-average and micro-average area under the precision-recall curve, as compared to the conventional ones. In addition, the top five informative graph features for RTCDs are found to be Transitivity, Edge, Node, Degree and Reciprocity. In view of interpretability, the basic, single-, dyadic- and triadic-node and global characteristics of clickstream graphs have their specific advantages.

The findings suggest that the temporal graph-theoretic approach can form an efficient and powerful AI-based real-time intent detecting decision-support system. Different levels of graph features have their specific interpretability on RTCDs from the perspectives of consumer behavior and psychology, which provides a theoretical basis for the design of computer information systems and the optimization of the ongoing session intervention or recommendation in e-commerce.

To the best of the authors' knowledge, this is the first study to apply clickstream graphs and real-time decision choices in conversion prediction and detection. Most studies have only meditated on a binary classification problem, while this study applies a graph-theoretic approach in a five-class classification problem. In addition, this study constructs temporal item-level graphs to represent the original structure of clickstream session data based on graph theory. The time-varying characteristics of the proposed approach enhance the performance of purchase conversion detection during an ongoing session.

The purpose of this paper is to propose a semiparametric estimator for the tail index of Pareto-type random truncated data that improves the existing ones in terms of mean square error. Moreover, we establish its consistency and asymptotic normality.

To construct a root mean squared error (RMSE)-reduced estimator of the tail index, the authors used the semiparametric estimator of the underlying distribution function given by Wang (1989). This allows us to define the corresponding tail process and provide a weak approximation to this one. By means of a functional representation of the given estimator of the tail index and by using this weak approximation, the authors establish the asymptotic normality of the aforementioned RMSE-reduced estimator.

In basis on a semiparametric estimator of the underlying distribution function, the authors proposed a new estimation method to the tail index of Pareto-type distributions for randomly right-truncated data. Compared with the existing ones, this estimator behaves well both in terms of bias and RMSE. A useful weak approximation of the corresponding tail empirical process allowed us to establish both the consistency and asymptotic normality of the proposed estimator.

A new tail semiparametric (empirical) process for truncated data is introduced, a new estimator for the tail index of Pareto-type truncated data is introduced and asymptotic normality of the proposed estimator is established.

The purpose of this paper extends the authors’ previous contributions on aircraft system identification, such as open loop identification or closed loop identification, to cascade system identification. Because the cascade system is one special network system, existing in lots of practical engineers, more unknown systems are needed to identify simultaneously within the statistical environment with the probabilistic noises. Consider this problem of cascade system identification, prediction error method is proposed to identify three unknown systems, which are parameterized by three unknown parameter vectors. Then the cascade system identification is transferred as one parameter identification problem, being solved by the online subgradient descent algorithm. Furthermore, the nonparametric estimation is proposed to consider the general case without any parameterized process. To make up the identification mission, model validation process is given to show the asymptotic interval of the identified parameter. Finally, simulation example confirms the proposed theoretical results.

Firstly, aircraft system identification is reviewed through the understanding about system identification and advances in control theory, then cascade system identification is introduced to be one special network system. Secondly, for the problem of cascade system identification, prediction error method and online subgradient decent algorithm are combined together to identify the cascade system with the parameterized systems. Thirdly from the point of more general completeness, another way is proposed to identify the nonparametric estimation, then model validation process is added to complete the whole identification mission.

This cascade system corresponds to one network system, existing in lots of practice, such as aircraft, ship and robot, so it is necessary to identify this cascade system, paving a way for latter network system identification. Parametric and nonparametric estimations are all studied within the statistical environment. Then research on bounded noise is an ongoing work.

To the best of the authors’ knowledge, research on aircraft system identification only concern on open loop and closed loop system identification, no any identification results about network system identification. This paper considers cascade system identification, being one special case on network system identification, so this paper paves a basic way for latter more advanced system identification and control theory.

In this study, fabrication of polymer and cotton fabric exhibiting stimuli-responsive wetting and water vapor permeability features together with antibacterial activity was aimed.

Temperature and pH-responsive poly(N-isopropyl acrylamide-graft-chitosan) (PNIPAM-g-CS) copolymer were produced via the free radical addition polymerization method and fixed to the cotton fabric using butane tetracarboxylic acid (BTCA) cross-linker by double-bath impregnation method. The chemical structure of the graft copolymer was characterized by Fourier-transform infrared spectroscopy (FT-IR) spectroscopy and H-Nuclear magnetic resonance (1H NMR) analyses. Thermo-responsive behavior of the fabric was investigated by wetting time and water uptake tests, contact angle measurement and surface energy calculation. Additionally, antibacterial activity of the fabric treated with copolymer was studied against S. aureus bacterium.

PNIPAM-g-CS graft copolymer was synthesized successfully, which had lower critical solution temperature (LCST) value of 32 °C and exhibited thermo-responsive property. The treated fabrics exhibited hydrophilic character at temperatures below the LCST and hydrophobic character at temperatures above the LCST. It was found that polymer-coated fabric could have regulated the water vapor permeability by the change in its pore size and hydrophilicity depending on the temperature. Additionally, treated fabric displayed a pH-responsive water absorption behavior and strong antibacterial activity against S.aureus bacterium.

In the study, it has been shown that the cotton fabrics can be fabricated which have antibacterial activity and capable of pH and temperature responsive smart moisture/water management by application of copolymer. It is thought that the fabric structures developed in the study will be promising in the production of medical textile structures where antibacterial activity and thermophysiological comfort are important.

This article aims to advance a new theoretical perspective on the basis of prosumption theory, namely, that online review platforms can be conceptualized as prosumer communities (and online reviews as prosumer-generated content). This perspective meshes with message tuning research to suggest specific mechanisms through which peer-to-peer prosumption takes place in online review communities. Overall, this article enriches and deepens theoretical understanding of prosumption behavior in the product review context and offers practical advice for inducing high-value, prosumer-generated content in online communities.

Exploratory observations of current practices across a wide spectrum of review platforms were conducted. The observed platforms include independent review sites (e.g. Yelp) and review sites affiliated with e-tailers (e.g. Amazon), general review sites (e.g. Viewpoints) and product-specific review sites (e.g. Healthgrades), large-scale review sites (e.g. TripAdvisor) and review sites of a smaller scale (e.g. Judy’s Book) and review sites based in different geographic regions, including Australia (e.g. Productreview.com.au), China (e.g. Taobao), Europe (e.g. Reevoo), India (e.g. Zomato) and North America (e.g. Foursquare).

Theoretical analysis suggests that high-quality review content is the result of collaborative prosumption characterized by three distinct value-adding processes: history-based message tuning, audience-based message tuning and norm-based message tuning. In-depth observations reveal that today’s review platforms are leveraging these value-adding processes to varying degrees. The overwhelming diversity of the observed platform features points to the need for more research on platform design and management.

This research identifies three distinct dimensions of review quality – novelty, relevancy and congruency – that can be systematically managed through platform design. The exploratory nature of this research necessitates follow-up work to further investigate how high-quality review content emerges in the historical, interpersonal and cultural contexts of online prosumer communities.

The prosumption-inducing mechanisms identified in this research have major consumer welfare and strategy implications. First, they may lead to novel, relevant and congruent consumer reviews. Second, they may enhance the value of brand communities (which rely heavily on collaborative prosumption).

This research addresses two intriguing questions pertinent to marketing theory and practice in the digital era. First, how do high-quality reviews emerge on product review platforms (which consist of ordinary consumers)? Second, what constitutes high-quality review content and how can platform managers facilitate the collaborative creation of high-quality review content by ordinary consumers?

The occurrence of COVID-19 has impacted the wide-reaching dimensions of manufacturing, materials, procurement, management, etc., and has loaded disruptions in the wide range of supply chain (SC) activities. The impact of COVID-19 has twisted supplier performance and influenced stakeholders’ thinking towards selecting supplier sources and making strategic sourcing decision for convinced arrangement of construction management (CM) resources. Nowadays, suppliers are intently evaluated by stakeholders in post-COVID-19 phase to induce agile availability of CM resources. Accordingly, this paper aims to demonstrate competent CM dimensions under post COVID-19 scenario for ease managing construction projects by the stakeholders.

The authors have implicated Grey Sets Theory along with decision-making trial and evaluation laboratory (DEMATEL) technique for understanding significant outcomes. Varieties of diverse decision aspects responsible for strategically influencing supplier sourcing decision is projected under post COVID-19 scenario for handling construction projects by the stakeholders.

This study investigated sustainable construction management dimensions (SCMD) at the stage of resource deliveries and client aspirations under post COVID-19 situation. The study demonstrated “Lead time” as the most crucial, “Product Range” as the second and “Customers dealings and relationship” as the third crucial aspect considering by the stakeholders for selecting supplier sources based on the attainment of performance score of 0.1338, 0.1273 and 0.1268, respectively. It is found that high lead time stimulates the stakeholders to divert their orders to other competent supplier sources holding a low degree of lead time as compared.

The present study rollovers its existence by serving critical thinking, conceptual modelling, criteria identification and evaluation under CM domain for drafting effectual strategies by the suppliers. The study investigated the impact of COVID-19 on stakeholders’ decision-making and enlisted SCMD that strategically stimulated them in choosing supplier sourcing decision.

The present study realizes the insights of stakeholders in the post COVID-19 scenario related to the supplier sources based on performance score. The study quantified sustainable supplier attribute for construction work and practices. The study analysed the expectations of the stakeholders purchasing different varieties of construction materials from supplier sources for civil works in the post COVID-19 scenario.

Previous research has demonstrated that affirming an individual’s self-worth in intrinsic, stable aspects (e.g. personal attributes) enhances their pro-relationship tendencies, as compared to affirming extrinsic aspects of the individual (e.g. performance). This is especially so among people in certain dissatisfying relationships (e.g. romantic relationships). Extending this finding to organizational contexts, the purpose of this study is to investigate the effects of affirmation type (intrinsic vs extrinsic affirmations) on responses to workplace offenses among employees with high versus low job satisfaction.

Studies 1 (N = 224) and 2 (N = 358) examined the effects of intrinsic versus extrinsic affirmations on responses to hypothetical and real workplace offenses. Furthermore, to compare the effects of intrinsic and extrinsic affirmations to the baseline level, Study 3 (N = 441) added a control condition and examined the effects of affirmation type (intrinsic vs extrinsic vs control) on responses to workplace offenses.

For employees with low (but not high) job satisfaction, (1) intrinsic (vs extrinsic) affirmations promoted more prosocial responses (forgiveness and reconciliation) to workplace offenses; (2) although not as effective as intrinsic affirmations, extrinsic affirmations (vs baseline) also triggered prosocial intentions toward workplace offenses.

First, the study enriches the literature on workplace offenses by focusing on an individual-level factor – self-worth – that can be intervened (e.g. affirming one’s self-worth) by organizations and managers so as to promote prosocial responses to workplace offenses. Second, the study expands the scope of the self-affirmation theory in organizational contexts by examining the effectiveness of intrinsic and extrinsic affirmations in coping with workplace offenses. Third, practically speaking, the study provides a brief intervention (the writing task of describing an intrinsic or extrinsic affirmation experience) that can boost pro-relationships in the workplace.

The purpose of this study is to investigate the two-dimensional unsteady inclined stagnation point flow and thermal transmission of Maxwell fluid on oscillating stretched/contracted plates. First, based on the momentum equation at infinity, pressure field is modified by solving first-order differential equation. Meanwhile, thermal relaxation characteristic of fluid is described by Cattaneo–Christov thermal diffusion model.

Highly coupled model equations are transformed into simpler partial differential equations (PDE) via appropriate dimensionless variables. The approximate analytical solutions of unsteady inclined stagnation point flow on oscillating stretched and contracted plates are acquired by homotopy analysis method for the first time, to the best of the authors’ knowledge.

Results indicate that because of tensile state of plate, streamline near stagnation point disperses to both sides with stagnation point as center, while in the case of shrinking plate, streamline near stagnation point is concentrated near stagnation point. The enhancement of velocity ratio parameter leads to increasing of pressure variation rate, which promotes flow of fluid. In tensile state, surface friction coefficient on both sides of stagnation point has opposite symbols; when the plate is in shrinkage state, there is reflux near the right side of the stagnation point. In addition, although the addition of unsteady parameters and thermal relaxation parameters reduce heat transfer efficiency of fluid, heat transfer of fluid near the plate can also be enhanced by considering thermal relaxation effect when plate shrinks.

First, approximate analytical solutions of unsteady inclined stagnation point flow on oscillating stretched and contracted plates are researched, respectively. Second, pressure field is further modified. Finally, based on this, thermal relaxation characteristic of fluid is described by Cattaneo–Christov thermal diffusion model.