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The textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.

This research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.

The results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.

This paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.

This study aims to evaluate the dynamics between healthcare resource capacity expansion and disease spread. Further, the study estimates the resources required to respond to a pandemic appropriately.

This study adopts a system dynamics simulation and scenario analysis to experiment with the modification of the susceptible exposed infected and recovered (SEIR) model. The experiments evaluate diagnostic capacity expansion to identify suitable expansion plans and timelines. Afterwards, two popularly used forecasting tools, artificial neural network (ANN) and auto-regressive integrated moving average (ARIMA), are used to estimate the requirement of beds for a period when infection data became available.

The results from the study reflect that aggressive testing with isolation and integration of quarantine can be effective strategies to prevent disease outbreaks. The findings demonstrate that decision-makers must rapidly expand the diagnostic capacity during the first two weeks of the outbreak to support aggressive testing and isolation. Further, results confirm a healthcare resource deficit of at least two months for Delhi in the absence of these strategies. Also, the study findings highlight the importance of capacity expansion timelines by simulating a range of contact rates and disease infectivity in the early phase of the outbreak when various parameters are unknown. Further, it has been reflected that forecasting tools can effectively estimate healthcare resource requirements when pandemic data is available.

The models developed in the present study can be utilised by policymakers to suitably design the response plan. The decisions regarding how much diagnostics capacity is needed and when to expand capacity to minimise infection spread have been demonstrated for Delhi city. Also, the study proposed a decision support system (DSS) to assist the decision-maker in short- and long-term planning during the disease outbreak.

The study estimated the resources required for adopting an aggressive testing strategy. Several experiments were performed to successfully validate the robustness of the simulation model. The modification of SEIR model with diagnostic capacity increment, quarantine and testing block has been attempted to provide a distinct perspective on the testing strategy. The prevention of outbreaks has been addressed systematically.

All hypothesized relationships were significant. Specifically, H1 was supported as disconfirmation and surprising consumption were significantly correlated. Moreover, arousal (H2) and outrage (H4a) were functions of surprising consumption and negative affect (H3) and outrage (H4b) were functions of arousal. H4c was also supported as negative affect had a significant direct effect on consumer outrage. In addition, disconfirmation had negative direct effects on both negative affect (H5) and dissatisfaction (H6a) and dissatisfaction was a function of negative affect. Finally, both outrage (H7a) and dissatisfaction (H7b) had significant negative effects on behavioral intentions.

Respondents were recruited to participate in the data collection in a “college town” in the Southeastern United States. Respondents were provided a paper and pencil data collection instrument that include complete survey instructions and the balance of the research design. To adequately test all hypotheses, the researchers developed a unique scenario that described an extreme service failure that takes place during a hotel check-in. Each respondent was asked to read the scenario and then reflect upon it as they responded to items that assessed their feelings toward the hotel check-in experience.

The results provide additional evidence in support of the existence of the satisfaction-dissatisfaction continuum, as well as specifically identifying the affective nature of levels of satisfaction that fall surprisingly well-below the zone of tolerance. The authors feel that the present study is a necessary step to provide a more comprehensive view of the satisfaction-dissatisfaction continuum. Likewise, the authors posit initial evidence of the antecedents and consequences of consumer outrage. This research supports the prior assumptions of Westbrook (1987) about the vast detrimental effects of negative affective responses to service or product failures.

Future research needs to discover just how extremely deficient service has to be to elicit outrage. Is outrage a personal phenomenon with every consumer experiencing it to different degrees? As such, is outrage triggered at different points on the satisfaction-dissatisfaction continuum? The zone of tolerance seems to suggest this, but it would be interesting to discover if at some collective level of dissatisfaction consumers generally begin to show signs of outrage. Likewise, it would be interesting to understand how the level and pattern of outrage results in customers exiting the relationship but also results in loyal customers becoming enemies (e.g. Gregiore et al., 2009; Gregiore and Fisher, 2008).

The motivation for the current study is both pragmatic and theoretical. As alluded to above, it is evident that the level of service customers’ emotional responses to their service experiences are increasing in frequency and intensity. These negative emotions affect the efficacy of service workers and impede the financial performance of service providers. The popular mantra of “anti-woke” consumers, “Go Woke, Go Broke,” is indicative of the importance of negative emotion. Sometimes referred to as “brand activism” (Moorman, 2020; Sarkar and Kotlet, 2019), recent public stances on social and political issues have led to a boycott of Gillette razors, the burning of Nike shoes, and the canceling of Costco Memberships in what has been called “virtue signaling” (Vredenburg et al., 2020). While none of these actions are desirable, the importance of investigating the impact of strong negative emotions (i.e. outrage) is further demonstrated in reports that 65% of consumers expect companies to authentically support such issues (Barton et al., 2018; Edelman, 2018; Larcker and Tayan, 2018; Moorman, 2020).

This research paper aims to empirically explore how stock market investors’ perceptions are affected by extreme climatic events like El Nino and floods in Malaysia.

This study uses structural equation modelling (SEM) to analyse the empirical data gathered through a questionnaire survey involving 273 individual investors from Bursa Malaysia between January and June 2019.

Results reveal that companies’ efforts, especially for agriculture and plantation-based industries, to adapt to climate change risk at the production, business and stock market levels significantly impact investors’ behaviour and investment decisions. Moreover, stock market investors’ climate change knowledge shows a significant moderating effect on corporate climate change adaptation initiatives and investors’ decisions to invest in Malaysian agricultural and plantation industry stocks.

This research has significant implications for practice and policy, as it measures the stock market investors’ level of awareness about climate change events and explores the companies’ strategies to reduce climatic risks to their business model.

This study shows the way to adjust the climate change information in the stock market investment decision to improve market efficiency and sustainable stock exchanges initiative.

To the best of the authors’ knowledge, this paper is the pioneer one to provide a comprehensive link between climate change events and business performances at production level, business level and stock market levels by drawing inferences from empirical data on investors’ behaviours. This study also added value in investment theories and financial literature by observing the climate change as an important factor to determine the investors’ decisions in the stock market.

This paper aims to develop an efficient algorithm combining straightforward response surface functions with Monte Carlo simulation to conduct seismic reliability analysis in a systematical way.

The representative slip surfaces are identified and based on to calibrate multiple response surface functions with acceptable accuracy. The calibrated response surfaces are used to determine the yield acceleration in Newmark sliding displacement analysis. Then, the displacement-based limit state function is adopted to conduct seismic reliability analysis.

The calibrated response surface functions have fairly good accuracy in predicting the yield acceleration in Newmark sliding displacement analysis. The seismic reliability is influenced by such factors as PGA, spatial variability and threshold value. The proposed methodology serves as an effective tool for geotechnical practitioners.

The multiple sources of a seismic slope response can be effectively determined using the multiple response surface functions, which are easily implemented within geotechnical engineering.

Work from home has become as regular as the traditional commuting system after the outbreak of the COVID-19 pandemic. Previous studies have discussed the influence of working at home on the work–family interface. However, there is limited understanding of how diverse workforces manage their work–family issues with various family-friendly policies. This study aims to bridge this research gap by examining the collective influence of work conditions and family-friendly policies on work–family balance.

A survey experiment featuring two working conditions (work from home or commuting) × four family-friendly policies (household subsidy, family-friendly supervisor, financial profit, paid leave vs no policy) was approached based on 703 valid responses in China.

The results indicate that family-friendly policies are more effective under the work-from-home condition than the commuting condition, household subsidies and financial profits are considered more helpful for work–family balance under the work-from-home condition and employees’ policy preferences depend on personal identity and work conditions, which help them maintain work and family issues concurrently.

This study explores the joint impact of work conditions and family-friendly policies from a situational perspective. This study indicated that professional organizations need to perform delicacy management considering policy preferences. Moreover, changing working arrangements help employees facilitate their work–family balance.

This study aims to explore the effects of expertise diversity on project efficiency and creativity in health-care project teams.

This study analyzes hierarchical linear models using multi-source data from 50 project teams in a large health-care organization in the USA. This data set includes self-reported survey responses from 274 team members and human resource information for all 515 members across the 50 teams. Expertise diversity is operationalized by professional diversity and positional diversity reflecting two dimensions, domain and level, of the concept of expertise.

This study reveals that professional diversity is negatively related to project efficiency and project creativity, whereas positional diversity is positively related to project efficiency.

Successfully managing a project team of experts within a limited time frame is a challenge for organizations. This study advances the understanding of the double-edged sword effect of expertise diversity on project teams, focusing on professional and positional diversity. It provides important insights for human resource development in terms of the composition of project teams regarding members’ expertise.

Contractor selection is critical in green buildings (GBs) since the preferred contractor has the responsibility to achieve construction sustainability as well as relationship sustainability. The developer satisfaction reflecting requirements can boost the cooperative relationship among stakeholders and act as an evaluation scale for the success of GB projects, which needs to be emphasized in the selection process but little involved in the existing research. This study explores improving GB contractor (GBC) selection by integrating developer satisfaction into selection procedures.

A systematic framework of GBC selection including twenty-five criteria from literature review and experts survey is firstly constructed. Both tactical and strategic criteria are further classified into Kano categories (must-be, one-dimensional, and attractive categories) using the fuzzy Kano model (FKM), and weighted by the developer satisfaction index. The model proposed by this study combining FKM and TOPSIS divides the selection process into the filtration phase and selection phase by Kano categories. The proposed model is finally verified through performance comparison among multiple methods in a case.

Selection criteria are measured linearly and nonlinearly, showing criteria having nonlinear satisfaction change accounts for two-thirds of all. Criteria at tactical level tend to be must-be or one-dimensional categories for the developer, and most strategic criteria are classed as the attractive category, indicating that adding strategic criteria is necessary for long-term cooperation. The proposed model, using developer satisfaction to improve the selection process, ensures the selected GBC to be the most satisfactory with requirements of the developer and makes the performance of GBCs easily distinguishable.

This study contributes to the existing body of knowledge for promoting relationship sustainability by supplementing an integrated model with emphasis on developer satisfaction in GBC selection, so as to establish a good initial foundation due to the match between performances of GBCs and needs of developers. It not only helps maximize developer satisfaction in GBC selection by applying satisfaction to pre-construction management, but also instructs GBCs to prioritize performance improvements. The framework is also conducive for developers to classify selection criteria and select other participants (like green suppliers) from the satisfaction perspective in GBs.

On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.

First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.

Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.

This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.

1. The stability of electricity consumption is important to the stable operation of the grid.

2. An improved FP-growth algorithm is employed to explore the influencing factors.

3. The improved algorithm enables the mining of rules containing specific attribute labels.

4. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

The stability of electricity consumption is important to the stable operation of the grid.

An improved FP-growth algorithm is employed to explore the influencing factors.

The improved algorithm enables the mining of rules containing specific attribute labels.

Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

The objective of this research is to identify the economic, demographic, sanitary and even cultural factors which explain the variability in the cross-section of returns in different markets globally during the first weeks after the outbreak of COVID-19.

Building on the event study methodology and using seemingly unrelated equations, the authors created several indicators on the impact of the pandemic in 75 different markets. Then, and using cross-sectional regressions robust to heteroscedasticity and using an algorithm to select independent variables from more than 30 factors, the authors determine which factors were behind the different stock market reactions to the pandemic.

Higher currency depreciation, inflation, interest rate or government deficit led to higher returns, while higher life expectancy, ageing population, GDP per capita or health spending led to the opposite effect. However, the positive effect of competitiveness and the negative effect of income inequality stand out for their statistical and economic significance.

This research provides a global view of investors' reaction to an extreme and unique event. Using a sample of 75 capital markets and testing the relevance of more than 30 variables from all categories, it is, to the authors' knowledge, the largest and most ambitious study of its kind.