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Corporate social responsibility communication strategies of hotel companies
No full textThe perception of motivation for corporate social responsibility (CSR) activities among individuals plays a crucial role in achieving successful outcomes. Therefore, it is essential to gain an understanding of how different CSR activities can affect the perception of motivation for corporate social responsibility, with a view to ensuring the success of a hotel's CSR strategy. This study aims to explore the effects of proactive and reactive communication strategies employed by hotel companies in relation to CSR, with a view to identifying how these strategies impact perceptions of CSR motivation and the subsequent influence on company outputs. To facilitate comparison between the two groups, an experimental design utilizing a fictional hotel brochure and two contrasting newspaper articles has been created. The newspaper articles have been fictionalized in accordance with both proactive and reactive CSR communication strategies. The study sample comprises 406 participants, and the data has been analyzed using structural equation modelling and t-tests. The findings indicate that perceptions of CSR motivation play a pivotal role in hotel evaluation. The proactive scenario is perceived as more value-driven by the first group, whereas in the second group, the reactive scenario is regarded as more strategy-oriented. Furthermore, the proactive group exhibits relatively more positive evaluations of firms in comparison. Consequently, there is an imperative for hotel companies to consider the interconnections between these variables when designing their CSR communication strategies.Social Sciences, Interdisciplinar
Remembering Cinematic Sequences: Boundaries Disrupt Memory in Fast-Paced Visual Events
No full textWe engage with at least one type of visual media on a daily basis. Among those, there is a growing interest in the perception of cinematic events among cognitive psychologists. The current study investigated how event boundaries and pace affect recognition memory for movie scenes. We presented participants with brief clips composed out of six shots which either included a boundary or not and whether the average shot length was long or short. The results indicated that slower paced scenes were remembered better than faster paced scenes. More interestingly, there was a significant interaction between event boundary and pace. For fast-paced scenes, lower accuracy as well as longer reaction times were observed for scenes that involved an event boundary compared to those without an event boundary. Analysis of the serial position of the individual shots further indicated that people remember information in the new scene compared to the old scene only for fast-paced scenes. Event segmentation theory states that we form an active model of an event in working memory, which is updated when there is a significant change that violates predictions. Our experiment adds to event segmentation theory suggesting that the role of event boundaries is conditional on the exposure duration. When information is consolidated with enough exposure, the experience of an event boundary does not hinder memory. The current study provides new evidence showing that in complex visual scenes, memory operates economically to rely on the current model when the resources are limited.Humanities, Multidisciplinary || Psychology, Experimenta
Dual-Band 2 x 1 Monopole Antenna Array and Its MIMO Configuration for WiMAX, Sub-6 GHz, and Sub-7 GHz Applications
No full textThis study introduces a cost-effective monopole antenna array and its MIMO configuration. The single element consists of a rectangular patch monopole featuring five circular slots at the center, accompanied by two thin slots at the top, offering a wide bandwidth (2-7.62 GHz) and a peak gain of 3.8 dBi. For gain improvement, a 2 x 1 antenna array is demonstrated. This antenna array exhibits dual-band behavior || spans from 2 to 3.71 GHz and from 5.9 to 7.54 GHz || covers the 2.5 GHz band (2.3-2.7 GHz), a significant portion of the n78 band (3.3-3.71 GHz), and the n96 band (5.925-7.125 GHz) || and is assigned to WiMAX, sub-6 GHz, and sub-7 GHz applications, respectively. The antenna array achieves a peak gain of 6.47 dBi. Lastly, a two-element MIMO configuration derived from the 2 x 1 array is designed. Implementing a defected ground structure (DGS) on the ground plane plays a crucial role in enhancing the isolation from 7 dB to 20 dB. The presented MIMO antenna covers the desired frequency bands of 2.5 GHz, n78, and n96 with a peak gain of 7.5 dBi and high radiation efficiency (<99%), which qualifies it for WiMAX, sub-6 GHz, and sub-7 GHz applications.Computer Science, Information Systems || Engineering, Electrical & Electronic || Physics, Applie
Predictive and prescriptive analytics for ESG performance evaluation: A case of Fortune 500 companies
No full textGiven the growing importance of organizations' environmental, social, and governance (ESG) performance, studies employing AI-based techniques to generate insights from ESG data for investors and managers are limited. To bridge this gap, this study proposes an AI-based multi-stage ESG performance prediction system consolidating clustering for identifying patterns within ESG data, association rule mining for uncovering meaningful relationships, deep learning for predictive accuracy, and prescriptive analytics for actionable insights. This study is grounded in the big data analytics capability view that has emerged from the dynamic capabilities theory. The model is validated using an ESG dataset of 470 Fortune listed 500 companies obtained from the Refinitiv database. The model offers practical guidance for decision-makers to maintain or enhance their ESG scores, crucial in a business landscape where ESG metrics significantly affect investor choices and public image.Busines
Combinatorial optimization methods for yarn dyeing planning
No full textManaging yarn dyeing processes is one of the most challenging problems in the textile industry due to its computational complexity. This process combines characteristics of multidimensional knapsack, bin packing, and unrelated parallel machine scheduling problems. Multiple customer orders need to be combined as batches and assigned to different shifts of a limited number of machines. However, several practical factors such as physical attributes of customer orders, dyeing machine eligibility conditions like flotte, color type, chemical recipe, and volume capacity of dye make this problem significantly unique. Furthermore, alongside its economic aspects, minimizing the waste of natural resources during the machine changeover and energy are sustainability concerns of the problem. The contradictory nature of these two makes the planning problem multi-objective, which adds another complexity for planners. Hence, in this paper, we first propose a novel mathematical model for this scientifically highly challenging yet very practical problem from the textile industry. Then we propose Adaptive Large Neighbourhood Search (ALNS) algorithms to solve industrial-size instances of the problem. Our computational results show that the proposed algorithm provides near-optimal solutions in very short computational times. This paper provides significant contributions to flexible manufacturing research, including a mixed-integer programming model for a novel industrial problem, providing an effective and efficient adaptive large neighborhood search algorithm for delivering high-quality solutions quickly, and addressing the inefficiencies of manual scheduling in textile companies || reducing a time-consuming planning task from hours to minutes.Engineering, Industrial || Engineering, Manufacturing || Operations Research & Management Scienc
Different types of approximation operators on Gn-CAS via ideals
No full textA mathematical approach to dealing with the problems of ambiguity and indeterminacy in knowledge is called a rough set theory. It begins by using an equivalence relation to divide the universe into parts. Numerous generalized rough set models have been developed and investigated to increase their adaptability and extend their range of applications. In this context, we introduce new generalized rough set models that are inspired by covering-based rough sets and ideals. In this paper, lower and upper approximations of new types of covering rough sets based on j-neighborhoods, complementary j-neighborhoods, and j-adhesions are defined via ideals. The main features of these approximations are examined. The relationships among them are given by various examples and propositions. Some comparisons between our methods and others' methods such as Abd El-Monsef et al.'s method [2] and Nawar et al.'s method [22] are given. A practical example is given to illustrate one of our methods is more precise.Mathematics, Applied || Mathematic
Introduction to the Special Issue Turkey, Asia, and the EU in a Changing Global Order
No full textThe special issue Turkey, Asia, and the EU in a Changing Global Order explores Turkey's pivot towards Asia amidst a slowdown in its EU accession. It delves into Turkey's increasing relations with Asia and its consequences for Turkey-EU relations. The issue also examines how Turkey's growing ties with Asian actors affect its relationship with the EU, pondering whether these developments are competitive or complementary to Western interests within the framework of global capitalism, providing critical insights into the evolving geopolitical landscape.Area Studie
Effects of Environment, Social, and Governance (ESG) Disclosures on ESG Scores: Investigating the Role of Corporate Governance for Publicly Traded Turkish Companies
No full textThe world has experienced climate-related issues, which increase the importance of ESG disclosures and corporate governance (CG) of companies, which take place at the heart of economies. Therefore, improving ESG disclosures and CG practices becomes significant to combat climate change at the company level. Considering that Turkiye restructured ESG disclosures in 2022, this study investigates the role of CG on the nexus between ESG scores of publicly traded companies (PTC) and ESG reports. So, the study analyzes 102 PTC (full sample), 51 PTC in Borsa Istanbul Corporate Governance Index (in-sample), and the remaining 51 PTC (out-sample) using ESG disclosures of 2022 and applying novel super learner (SL) algorithm. Our results show that (i) SL has a higher prediction performance reaching similar to 94.3% || (ii) the environment (governance) layer has the highest (lowest) total relative importance (contribution) to ESG scores in all samples || (iii) C8, S6, and E5 are the most important ESG principles in the full sample, in-sample, and out-sample, respectively || (iv) the contribution of each ESG principles to the total ESG scores varies by sample || (v) CG plays a smoothing role for the relative importance of each ESG principle, while the relative importance in the out-sample shows much higher volatility. Overall, the study reveals the non-linear contributions of ESG principles on ESG scores and suggests that PTC should prioritize highly important ESG principles, consider the moderating role of CG on the link between ESG scores and ESG disclosures, and use ESG disclosures as a strategic tool to develop ESG scores and disclosures.Environmental Science
The Impact of Green Business Ethics and Green Financing on Sustainable Business Performance of Industries in Türkiye: The Mediating Role of Corporate Social Responsibility
No full textThe purpose of this research is to understand the relationship between green business ethics, green finance, and sustainable business performance, and to evaluate the role of corporate social responsibility (CSR) in this relationship. The impact of the damage inflicted on nature's functioning order is being felt much more strongly today. In light of these realities, companies must emphasize sustainability principles not just out of financial concerns but as a result of corporate social responsibility. In this context, focusing on the role of corporate social responsibility in sustainable business performance is the main goal of this research. Quantitative research methods, specifically the cross-sectional survey method, were employed for data collection and analysis. For this purpose, a convenience sampling method was used to select 427 white-collar employees working in industries operating in T & uuml;rkiye as the sample for this study. The data collected through surveys were analyzed using the AMOS 24 statistical program. The findings underscore that green business ethics and green finance have a significant impact on corporate social responsibility and sustainable business performance. Additionally, it was determined that corporate social responsibility plays an intermediary role in shaping sustainable business performance. These findings are expected to provide an important foundation that can guide both employees and managers in developing awareness about green policies and sustainability, emphasizing the importance of green policies in working life.Green & Sustainable Science & Technology || Environmental Sciences || Environmental Studie
An Empirical Evaluation of Feature Selection Stability and Classification Accuracy
No full textThe performance of inductive learners can be negatively affected by high -dimensional datasets. To address this issue, feature selection methods are used. Selecting relevant features and reducing data dimensions is essential for having accurate machine learning models. Stability is an important criterion in feature selection. Stable feature selection algorithms maintain their feature preferences even when small variations exist in the training set. Studies have emphasized the importance of stable feature selection, particularly in cases where the number of samples is small and the dimensionality is high. In this study, we evaluated the relationship between stability measures, as well as, feature selection stability and classification accuracy, using the Pearson 's Correlation Coefficient (also known as Pearson 's Product -Moment Correlation Coefficient or simply Pearson's r ). We conducted an extensive series of experiments using five filter and two wrapper feature selection methods, three classifiers for subset and classification performance evaluation, and eight real -world datasets taken from two different data repositories. We measured the stability of feature selection methods using a total of twelve stability metrics. Based on the results of correlation analyses, we have found that there is a lack of substantial evidence supporting a linear relationship between feature selection stability and classification accuracy. However, a strong positive correlation has been observed among several stability metrics.Multidisciplinary Science