Ready-made garments (RMG) are one of the most critical sectors in the economy of the South Asian region in terms of the labor force employed and export earnings. This research study aims to determine the Corporate Social Responsibility Stakeholders dimension and its influence on textile firms Performance. The study used organizational legitimacy as mediating variable between the CSR stakeholders and firms’ performances. The research study was used in the quantitative analysis approach to determine the cause and effect of the relationship between CSR and Textile firm’s financial and non-financial performance. Though the study collected primary data & secondary data from 250 respondents using survey questionnaires, the researcher obtained secondary data by analyzing the audited annual and sustainability reports of various RMG companies. We have collected data by conducting a focus group interview forming a team of employers, top-level managers, and CSR officers. We asked them all the questions, filled it, tapped it, reserved it for the interpretations. We have surveyed 67 industries, but it enabled us to collect the data from the 50 sectors—the data collected from 2016 April to 2018 December. Our study has some limitations in that the sample size is small compared to the other research. SPSS-23 & MS-Excel were used to analyze the collected data. CSR practices benefitted RMG companies in terms of long-term sustainable development by increasing the firm’s financial and non-financial performance of the RMG sector.
PurposeDrawn on self-determination (SDT) and social cognitive theory (SCT), this study examines how participative leadership (PL) influences the creative process engagement of followers (CPE) on fostering followers' radical creativity (FRC) through the supervisor support for creativity (SSC). It also demonstrates the CPE as a cognitive mediator between PL and FRC and SSC as a behavioral moderator between PL and CPE in Asia's manufacturing settings.Design/methodology/approachThe research is quantitative, and data are gathered using a questionnaire and a survey of Bangladesh's 252 textile and apparel industry respondents. SPSS 26 and SMART PLS 3.8 evaluated the measurement and structural models and other descriptive analyses for hypothesis testing and result confirmation.FindingsThe findings revealed that PL positively impacted followers' creative process engagement. Again, the CPE of followers was used to mediate PL and FRC to promote and determine radical creativity. Moreover, the research also found a substantial correlation between PL and the creative process involved in supervisor support for creativity, which increases followers' radical creativity.Research limitations/implicationsThis study contributes to the current literature by extending the scope of PL, CPE, FRC, SDT and SCT theory incorporating supervisor support.Practical implicationsThe findings showed that textile and apparel industry managers, leaders and practitioners could use participatory leadership to engage in collaborative leader-follower creativity goal setting, creativity-relevant thinking and talent flourishing to encourage and motivate creativity through supervisor support to followers to foster radical creativity.Originality/valueThe results demonstrate the colloquial expression in behavioral mechanism (creative process engagement) nurtured with the cognitive tool, shedding insight into the link between PL and radical creativity in followers (SSC for promoting radical creativity).
<span lang="EN-US">Brain is the most important part of the nervous system. Brain tumor is mainly a mass or growth of abnormal tissues in a brain. Early detection of brain tumor can reduce complex treatment process. Magnetic resonance images (MRI) are used to detect brain tumor. In this paper, we have introduced a deep convolutional neural network (CNN) to automatic brain tumor segmentation using MRI medical images which can solve the vanishing gradient problem. Classifying the brain MRI images with Resnet-50 and InceptionV3 in order to identify whether there is tumor or not. After this step, we have compared the accuracy level of both of the CNN models. Thereafter, applied U-Net architecture individually with encoder Resnet-50 and InceptionV3 to avieved promising results. The publicly available low grade gliomas (LGG) segmentation dataset has been utilized to test the model. Before applying the model on the MRI images preprocessing and several augmentation techniques have been done to obtain quality a dataset. U-net architecture with InceptionV3 provided 99.55% accuracy. On the other hand, our proposed method U-net with encoder ResNet-50 showed 99.77% accuracy.</span>
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