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1 – 10 of 15Saba Sareminia, Zahra Ghayoumian and Fatemeh Haghighat
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Robin Wakefield and Kirk Wakefield
Social media is replete with malicious and unempathetic rhetoric yet few studies explain why these emotions are publicly dispersed. The purpose of the study is to investigate how…
Abstract
Purpose
Social media is replete with malicious and unempathetic rhetoric yet few studies explain why these emotions are publicly dispersed. The purpose of the study is to investigate how the intergroup counter-empathic response called schadenfreude originates and how it prompts media consumption and engagement.
Design/methodology/approach
The study consists of two field surveys of 635 in-group members of two professional sports teams and 300 residents of California and Texas with political party affiliations. The analysis uses SEM quantitative methods.
Findings
Domain passion and group identification together determine the harmonious/obsessive tendencies of passion for an activity and explain the schadenfreude response toward the rival out-group. Group identification is a stronger driver of obsessive passion compared to harmonious passion. Schadenfreude directly influences the use of traditional media (TV, radio, domain websites), it triggers social media engagement (posting), and it accelerates harmonious passion's effects on social media posting.
Research limitations/implications
The study is limited by the groups used to evaluate the research model, sports, and politics.
Social implications
The more highly identified and passionate group members experience greater counter-empathy toward a rival. At extreme levels of group identification, obsessive passion increases at an increasing rate and may characterize extremism. Harboring feelings of schadenfreude toward the out-group prompts those with harmonious passion for an activity to more frequently engage on social media in unempathetic ways.
Originality/value
This study links the unempathetic, yet common emotion of schadenfreude with passion, intergroup dynamics, and media behavior.
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Shiva Kakkar, Samvet Kuril, Surajit Saha, Parul Gupta and Swati Singh
Employing the “Job demands-resources (JD-R)” framework, this study examines the impact of co-occurring social supports (supervisor, coworker, and family support) on the telework…
Abstract
Purpose
Employing the “Job demands-resources (JD-R)” framework, this study examines the impact of co-occurring social supports (supervisor, coworker, and family support) on the telework environment and employee engagement.
Design/methodology/approach
The study uses a multimethod approach. Data from 294 employees belonging to Indian technology organizations were collected and analyzed using the partial least squares (PLS)-based structure equation modeling software SmartPLS4. Following this, necessary condition analysis (NCA) was carried out using the NCA package for R.
Findings
Telework environment was found to mediate the relationship between social support and work engagement. Supervisor support and instrumental family support were identified as predictors as well as necessary conditions for telework environment. Coworker support was identified both as a predictor and necessary condition for telework environment. Although emotional family support was found to be a predictor of telework environment, it was not identified as a necessary condition.
Practical implications
The findings indicate that coworker support and family instrumental support are as important for telework success as supervisor support. Moreover, our findings suggest that varying levels of telework environments (low, moderate, and high) may necessitate distinct social support configurations. Consequently, organizations should match their social support configuration to match their overall teleworking strategy.
Originality/value
A basic premise of the JD-R framework is that resources exist in caravans (bundles). However, previous research (in telework) has concentrated on only one or two kinds of social support, that too in varying situational contexts, limiting generalizability of the findings. This has also produced inconsistent conclusions concerning the role of support providers such as coworkers and family. Recent developments in JD-R also suggest that the role of resources may vary in terms of their importance (necessity) for work engagement. By augmenting standard regression-based techniques with NCA, the authors explore these issues to provide a more thorough understanding of the influence of social supports on work engagement in telework situations.
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Debolina Dutta and Sushanta Kumar Mishra
The fear of the pandemic, confinement at home and the need to work created a unique situation. The pandemic catalyzed work-from-anywhere practice by adopting information and…
Abstract
Purpose
The fear of the pandemic, confinement at home and the need to work created a unique situation. The pandemic catalyzed work-from-anywhere practice by adopting information and communication technologies (ICT) across all industries. While ICT saved organizations, it increased technostress among the workforce. A better understanding of the adverse effects of ICT usage might enable organizations to manage the mental well-being of the workforce. While technostress is gaining increasing interest, scholarly work investigating the dimensions of technostress and its impact on creating stress across various employee demographics and industry types is missing. Contrary to the prevalent assumptions, the authors theorized and tested the adverse moderation effect of the home-work interface on the linkage between technostress dimensions and stress. This paper aims to discuss the aforementioned objective.
Design/methodology/approach
The study captures dimensions of technostress and the resulting stress at work using a survey-based analysis of 881 working employees in India, representing multiple industries and functions.
Findings
The study indicates that techno-overload, techno-complexity and techno-invasion significantly impact employees during the pandemic. The authors further found that the home-work-interface is a powerful factor in understanding the complex linkage between dimensions of technostress and its outcomes.
Research limitations/implications
Based on the Conservation of Resources Theory and the Job-Demand-Resources model, this study highlights the adverse impact of this trend on employee well-being. However, the study suffers from a cross-sectional research design. The technostress research has focused primarily on static, at-premise environments and mostly on high ICT usage industries. Due to the pandemic, it has neglected the impact of various technostress dimensions across employee cohorts subjected to rapid technology-enabled working. Further, most studies focus on the voluntary choice of remote work. Employees struggle with the unexpected and involuntary shift to technology-enabled remote work. This study contributes to the literature by examining the consequences of technostress in the context of non-voluntary remote work. Contrary to prevailing assumptions, this study highlights the adverse effect of organizational home-work interface in influencing ICT-created stress.
Practical implications
The increasing use of ICT enables telecommuting across the workforce while increasing organizational productivity. Due to the pandemic, these trends will likely change the future of work permanently. To minimize employee stress, practitioners need to reconsider the dimensions of technostress. Further, the study cautions against the prevalent interventions used by practitioners. While practitioners facilitate a home-work interface, it could have adverse consequences. Practitioners may consider the adverse consequences of home-work interface while designing organizational policies.
Social implications
This study during the pandemic is crucial as research forecasts the likelihood of other cataclysmic events, such as future pandemics and political or climate change events, which may sustain technology-driven remote work practices and remain a feature of the future workplace. Hence understanding the implications of the dimensions of technostress would help organizations and policymakers to implement necessary interventions to minimize employee stress.
Originality/value
The present study examines the dimensions of technostress across multiple industries and job functions in an emerging market marked by a high economic growth rate and an Eastern cultural context. This study presents the dark side of excessive ICT adoption and indicates how organizations and HRM practices can help mitigate some of these effects.
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Pedro L. Angosto-Fernández and Victoria Ferrández-Serrano
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…
Abstract
Purpose
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.
Design/methodology/approach
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.
Findings
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.
Originality/value
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.
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Michael Yao Ping Peng, Meng-Hsiu Lee and Ya-Hui Huang
The purpose of this study is to examine the relationship between positive emotion, self-efficacy, job satisfaction and turnover intention in the context of resource building…
Abstract
Purpose
The purpose of this study is to examine the relationship between positive emotion, self-efficacy, job satisfaction and turnover intention in the context of resource building during the socialization process of new faculty members, particularly in the context of the coronavirus disease 2019 (COVID-19) pandemic.
Design/methodology/approach
The study utilizes a quantitative research design and employs purposive sampling to obtain 554 valid questionnaires. The study analyzes the relationship between positive emotion, self-efficacy, job satisfaction and turnover intention and examines the influence of strategic human resource management (SHRM) on these variables.
Findings
The results of the study reveal that SHRM positively influences positive emotion and self-efficacy, which, in turn, positively impact job satisfaction. However, positive emotion is negatively related to turnover intention.
Originality/value
This study contributes to the existing literature on human resource management (HRM) by examining the impact of strategic HRM on the socialization process of new faculty members. The findings of the study have significant practical implications for the implementation of HRM in research-oriented universities.
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Fatemeh Mostafavi, Mohammad Tahsildoost, Zahra Sadat Zomorodian and Seyed Shayan Shahrestani
In this study, a novel framework based on deep learning models is presented to assess energy and environmental performance of a given building space layout, facilitating the…
Abstract
Purpose
In this study, a novel framework based on deep learning models is presented to assess energy and environmental performance of a given building space layout, facilitating the decision-making process at the early-stage design.
Design/methodology/approach
A methodology using an image-based deep learning model called pix2pix is proposed to predict the overall daylight, energy and ventilation performance of a given residential building space layout. The proposed methodology is then evaluated by being applied to 300 sample apartment units in Tehran, Iran. Four pix2pix models were trained to predict illuminance, spatial daylight autonomy (sDA), primary energy intensity and ventilation maps. The simulation results were considered ground truth.
Findings
The results showed an average structural similarity index measure (SSIM) of 0.86 and 0.81 for the predicted illuminance and sDA maps, respectively, and an average score of 88% for the predicted primary energy intensity and ventilation representative maps, each of which is outputted within three seconds.
Originality/value
The proposed framework in this study helps upskilling the design professionals involved with the architecture, engineering and construction (AEC) industry through engaging artificial intelligence in human–computer interactions. The specific novelties of this research are: first, evaluating indoor environmental metrics (daylight and ventilation) alongside the energy performance of space layouts using pix2pix model, second, widening the assessment scope to a group of spaces forming an apartment layout at five different floors and third, incorporating the impact of building context on the intended objectives.
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Nehal Elshaboury, Eslam Mohammed Abdelkader, Abobakr Al-Sakkaf and Ashutosh Bagchi
The energy efficiency of buildings has been emphasized along with the continual development in the building and construction sector that consumes a significant amount of energy…
Abstract
Purpose
The energy efficiency of buildings has been emphasized along with the continual development in the building and construction sector that consumes a significant amount of energy. To this end, the purpose of this research paper is to forecast energy consumption to improve energy resource planning and management.
Design/methodology/approach
This study proposes the application of the convolutional neural network (CNN) for estimating the electricity consumption in the Grey Nuns building in Canada. The performance of the proposed model is compared against that of long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks. The models are trained and tested using monthly electricity consumption records (i.e. from May 2009 to December 2021) available from Concordia’s facility department. Statistical measures (e.g. determination coefficient [R2], root mean squared error [RMSE], mean absolute error [MAE] and mean absolute percentage error [MAPE]) are used to evaluate the outcomes of models.
Findings
The results reveal that the CNN model outperforms the other model predictions for 6 and 12 months ahead. It enhances the performance metrics reported by the LSTM and MLP models concerning the R2, RMSE, MAE and MAPE by more than 4%, 6%, 42% and 46%, respectively. Therefore, the proposed model uses the available data to predict the electricity consumption for 6 and 12 months ahead. In June and December 2022, the overall electricity consumption is estimated to be 195,312 kWh and 254,737 kWh, respectively.
Originality/value
This study discusses the development of an effective time-series model that can forecast future electricity consumption in a Canadian heritage building. Deep learning techniques are being used for the first time to anticipate the electricity consumption of the Grey Nuns building in Canada. Additionally, it evaluates the effectiveness of deep learning and machine learning methods for predicting electricity consumption using established performance indicators. Recognizing electricity consumption in buildings is beneficial for utility providers, facility managers and end users by improving energy and environmental efficiency.
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Prince Yao Amu, Bedman Narteh and Prince Kodua
The purpose of this study is to identify which dimensions of perceived value best mediate football club branding and fan loyalty from a developing league perspective.
Abstract
Purpose
The purpose of this study is to identify which dimensions of perceived value best mediate football club branding and fan loyalty from a developing league perspective.
Design/methodology/approach
Using a cross-sectional design, we collected data using questionnaires from football fans in Ghana (N = 700). The data were analysed using SmartPLS V3, applying structural equation modelling with bootstrapping procedure.
Findings
The results indicate that club branding is an effective precursor of fan loyalty. Moreover, the findings suggest that functional, social and emotional values mediated club branding and fan loyalty, whereas epistemic and economic values did not.
Originality/value
This study contributes to sports management literature by identifying the dimensions of perceived value that will be relevant in the development of club brands in the developing league context.
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Xueqing Gan, Jianyao Jia, Yun Le and Yi Hu
Infrastructure projects are pivotal for regional economic development, but also face low project effectiveness. Leadership is always regarded as a key enabler for project team…
Abstract
Purpose
Infrastructure projects are pivotal for regional economic development, but also face low project effectiveness. Leadership is always regarded as a key enabler for project team effectiveness, including vertical leadership and team-level leadership. The purpose of this paper is to examine how vertical leadership facilitates shared leadership in infrastructure project teams.
Design/methodology/approach
This paper develops the conceptual model based on the literature review. Then the questionnaire survey was conducted. The empirical data obtained from 117 infrastructure project teams in China were analyzed by partial least squares structural equation modeling (PLS-SEM) for validating the proposed model. Finally, the results were comparatively discussed to explain the dual-pathway between vertical leadership and shared leadership. And the practical implications were presented for the project managers in infrastructure project teams.
Findings
Drawing on social learning theory and social cognitive theory, the results show that both participative leadership and task-oriented leadership can facilitate shared leadership. Further, team atmosphere fully mediates the link between participative leadership and shared leadership. Team efficacy fully mediates the relation between task-oriented leadership and shared leadership. Also, role clarity has a negative moderating effect on the former path.
Originality/value
The study extends the knowledge of leadership theory in the construction field. Based on the proposed conceptual model and PLS-SEM results, this study unveils the black box between vertical leadership and shared leadership and contributes to the theory of leadership on how the impact of different vertical leadership on team process promotes shared leadership.
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