Search results

The study evaluates the effects of GVC participation on firm productivity and profitability. Hence this study aims to find evidence whether there is a clear difference between the productivity and profitability effects of simple and complex backward and forward participations for Turkish firms.

The authors employ a firm level data from the Türkiye's both first and second top 500 industrial enterprises from 1993 to 2019. In addition, the authors calculate country-sector level both backward and forward GVC participation indices with their simple and complex sub-indices for each year from 1990 to 2015 from the Full Eora data of the Eora Global Supply Chain Database. The authors estimate the model with OLS and fixed effects. To understand the role of the 2008 global crisis, the authors also undertake estimations for the pre-crisis and post-crisis. The authors also divide the data by R&D intensity of sectors.

While backward GVC participation lowers both labor productivity and profitability growth, forward GVC participation promotes both. Moreover, simple and complex backward participation have similarly negative effects on productivity and profitability growth, simple and complex forward participation have the completely opposite effects though. The authors then provide substantial evidence for the differing effects of participation on productivity and profitability growth between pre-crisis and post-crisis periods. Interestingly, backward participation has a negative impact for both hi-tech and low-tech firms while forward participation boosts the productivity growth only for low-tech firms, probably due to the relatively more upstream position of low-tech firms.

To the best of the knowledge, no previous study has yet examined the profitability effects of GVC for firms. Second, in addition to overall backward and forward GVC participation rates, the authors also calculate and utilize simple and complex GVC measures in the estimations. Third, to reveal whether the global financial crisis leads to a shift in the productivity and profitability effects of GVCs, the authors separately run the regressions for the pre- and post-crisis periods. Fourth, the authors then investigate the argument that hi-tech sectors/firms could benefit more from joining GVCs compared to firms in low-tech technology sectors.

This paper explores the association between a variety of trade and government policy measures of both home and trade partners introduced during the Covid-19 pandemic within the gravity-like framework by using the Turkish bilateral trade statistics at the six-digit product level from February 2020 to March 2022.

The empirical model is estimated by employing the two-way fixed effects (TWFE) estimation technique to get rid of the effects of unobserved time-invariant idiosyncratic country-product effects in the model and to evade the possible autocorrelation problem of trade measures.

Our empirical analysis suggests that lower Turkish GVC participation during this period can be attributed to lower mobilities and the lack of vaccines globally. Our analysis utilizing the different income groups of countries and technology group of sectors suggests that Turkish high-tech backward participation with developed countries can be more sensitive to any changes in Covid-19-related policy measures, whereas Turkish forward participation with both developed and developing markets can be more volatile during the pandemic because of the containment measures.

Sustaining mobility in the global production process is the key finding to sustain both backward and forward GVC linkages. In addition, enhancing a variety of partner countries is crucial for sustaining the flows of imported intermediates of the Turkish manufacturing sectors. Moreover, the sophistication of Turkish exported products can be the solution to continue the forward GVC participation even in the shock times. Given the product and partner country-level heterogeneities regarding contingency measures implemented by the governments, policymakers should carefully monitor each sub-sample separately and focus especially on enhancement in information, communication and transportation infrastructures to mitigate the contagious effect of any external shocks.

The unique monthly six-digit bilateral product-level trade dataset enables us to observe and utilize heterogeneous effects at the product, sector and partner country levels.

Following the remarkable debut of ChatGPT and its rapid adoption by a global audience since its launch in November 2022, this study delves into educators' perceptions of ChatGPT within the specialized domains of tourism and hospitality education. While acknowledging ChatGPT’s swift rise and advanced capabilities, this research aims to comprehensively explore educators' perspectives, advantages and concerns regarding its integration into academic settings.

A qualitative approach was utilized to reveal dominant themes from in-depth, semi-structured face-to-face interviews with twenty lecturers in tourism faculties in North Cyprus. Collected responses from respondents were subjected to analysis using Leximancer software.

Our analysis unearthed seven significant themes encapsulating educators' perceptions of ChatGPT: 1 – “reliability and accuracy concerns”; 2 – “dependency”; 3 – “student engagement”; 4 – “ethical considerations”; 5 – “time efficiency and productivity”; 6 – “teacher-student interaction” and 7 – “learning flexibility”. Noteworthy positive perceptions emerged regarding “student engagement,” “time efficiency and productivity,” and “learning flexibility.”

This study contributes to the originality of research by addressing the underexplored aspect of educators' perceptions of ChatGPT within the domains of tourism and hospitality education, shedding light on its potential implications, advantages and drawbacks in a specialized educational context. Furthermore, it aims to offer insights into educators' recommendations for the effective incorporation of ChatGPT technologies into this specific educational setting, filling a crucial gap in understanding the integration of artificial intelligence (AI) in specialized fields of study.

This study aims to present a scaffolding framework incorporating sophisticated technology that can inform instructional design to support student inquiry learning in the self-regulated online environment.

The classic predict, observe and explain (POE) framework has been extended through the incorporation of an additional evaluate (E) phase into the model to enhance the self-regulated online learning environment. The extended POEE scaffolding approach, in this study, has been conceptualized as an implicit guide to support the process of guided inquiry for learning particular science concepts. Digital tools were sourced and integrated into this design framework to substitute for the support typically offered by teachers and peers in classrooms.

The findings suggest that the POEE pedagogical design facilitated the inquiry process through promoting self-regulation and engaged exploration. It also promoted positive emotions in students towards the scaffolded learning modules.

Integrating technologies that benefit students differentially in educational settings remains a considerable challenge. More specifically, in science education, an appropriate inquiry learning context that allows access to well informed pedagogical design is imperative. The application of this inquiry-based scaffolding framework can inform educators in the process of creating their own instructional designs and contexts to provide more effective guided learning.

The purpose of this paper is to propose a new integrated method for evaluating inventory of slow-moving items by introducing the application of fuzzy AHP method with interval Type-2 fuzzy sets (IT2FSs) and ABC analysis.

In the study, fuzzy analytic hierarchy process (AHP) method with IT2FSs is employed to set the importance of criteria. The weights obtained from IT2 fuzzy AHP are used to classify slow-moving items in ABC analysis. In the application part, a real-life case study is presented.

The result of this study indicates that an integrated approach utilizing IT2 fuzzy AHP and ABC analysis can be used as a supportive tool for classification of slow-moving items. The problem is solved under fuzzy environment to handle uncertainties and lack of information about slow-moving items.

Actual data are provided from an automotive company for prioritizing a various criteria to evaluate and classify stocks and a hypothetical model integrated with IT2 fuzzy AHP and ABC analysis is demonstrated.

Apart from inventory classification literature, the study integrates fuzzy AHP method by employing interval IT2FSs and ABC analysis to solve the real-life inventory classification problem.

This study aims to evaluate the suspicious transaction reporting (STR) as a financial intelligence tool to identify the potential strengths and limitations of STR and to come up with the criteria, which will make this tool an effective one in early detection of terrorist financing activities.

Considering the research aim, this research uses the funnelling method for identifying effectiveness criteria. Funnelling is a method of literature review that helps find pertinent literature by refining the search through filtering the available research (Ridley, 2008). Using this method, the researcher first applied the criteria of actionable intelligence to filter the financial intelligence tools to select the most promising and important tool (suspicious transaction reporting) for early detection of terrorist financing activities. The funnelling method was also applied to derive the effectiveness criteria from the operational features, and corresponding limitations, of the suspicious transaction reporting system. The funnelling method was also used to identify those operational features and limitations of suspicious transaction reporting that have the most direct relevance to the early detection problem of suspicious transaction reporting.

There are some operational features of STR that give rise to certain limitations that undermine its effectiveness in terms of early detection of terrorist financing activities. The limitations of STR necessitate a search for criteria that will make STR effective in early detection of terrorist financing activities. Based on the operational features and their corresponding limitations, effectiveness criteria for STR have been derived in this study. It is shown how these effectiveness criteria can remove the limitations of STR.

The list of operational features and the corresponding limitations based on which the effectiveness criteria have been derived may not be exhaustive. There may have other operational features, and corresponding limitations that also make STR largely ineffective in the early detection of terrorist financing activities, and for which more effectiveness criteria should also be derived.

The limitations and the effectiveness criteria will pave the way for redesigning STR in such a way that will make it highly useful for detecting financing activities relating to imminent terrorist attacks.

The society will experience fewer terrorist attacks that will make the society peaceful, happy and vibrant.

In this study, the effectiveness criteria of STR for early detection of terrorist financing activities have been derived in an innovative way by deducing them from the operational features of STR and the corresponding limitations.

The purpose of this article is to present the idea of a T-spherical hesitant fuzzy set associated with probability and to develop an extended multi-attributive border approximation area comparison (MABAC) method under probabilistic T-spherical hesitant fuzzy (Pt-SHF) settings.

The authors define some basic operational laws for Pt-SHF sets (Pt-SHFSs) and a comparison method of two probabilistic T-spherical hesitant fuzzy numbers (Pt-SHFNs) is proposed. Moreover, some Pt-SHF aggregation operators and the multi-attributive border approximation area comparison (MABAC) method are established under Pt-SHF scenario to solve group decision making problems.

The developed Pt-SHF MABAC method for multi-attribute group decision making (MAGDM) can overcome the drawbacks of conventional MABAC method and limitations for decision makers, which they face while providing their assessment concerning any object.

Clearly, this paper is devoted to MABAC method, MAGDM and probabilistic T-spherical hesitant fuzzy set theory.

The approach established can be used in a variety of scenarios, including decision making, engineering, and economics. An explanatory example is illustrated which shows the superiority and effectiveness of our proposed technique.

If a T-spherical fuzzy MAGDM problem under the probabilistic scenario needs to be evaluated, the involvement of probabilities in fuzzy system will be lost because of no information. This work fills a gap in literature by establishing the notion of probabilistic t-spherical hesitant fuzzy set to deal with the ambiguity, uncertainty in decision making problems.

In the realm of education, there has been an increasing emphasis on developing analytical thinking (AT) in the past few years. This systematic review focuses on an analysis of journal publications that have explored AT within the context of science, technology, engineering and mathematics (STEM) education. This analysis investigated four primary issues: (1) the operational definition that was used; (2) the types of theories that were used; (3) the interventions that were implemented to enhance AT skills; and (4) the research designs that were used.

To ensure a comprehensive and thorough review, we used the guidelines of preferred reporting items for systematic reviews and meta-analysis.

A comprehensive review of 28 pertinent scholarly articles reveals that scholars frequently rely on the concepts proposed by Anderson (2002), Marzano and Kendall (2008), Rodrangsee and Tuntiwongwanich (2021) and Suyatman et al. (2021) to establish a framework for delineating the competencies associated with analytical thinking (AT). Quasi-experimental designs were the most frequently used research designs in the studies analysed, followed by research and development approaches and then correlational designs. Most researchers have focused on investigating the effectiveness of problem-based learning as an intervention for improving AT skills. However, most research indicates that the theories or theoretical frameworks used to guide the research must be evident.

To the extent the authors know, this study represents the initial comprehensive examination of analytical thinking in STEM education. It presents a consolidated summary of the available evidence, assessing its quality and bringing it together in a single resource.

This paper aims to investigate the current state of biocomposites used in fused deposition modelling (FDM) with a focus on their mechanical characteristics.

The study presents a variety of biocomposite materials that have been used in filaments for 3D printing by different researchers. The process of making filaments is then described, followed by a discussion of the process parameters associated with the FDM.

To achieve better mechanical properties of 3D-printed parts, it is essential to optimize the process parameters of FDM while considering the characteristics of the biocomposite material. Polylactic acid is considered the most promising matrix material due to its biodegradability and lower cost. Moreover, the use of natural fibres like hemp, flax and sugarcane bagasse as reinforcement to the polymer in FDM filaments improves the mechanical performance of printed parts.

The paper discusses the influence of critical process parameters of FDM like raster angle, layer thickness, infill density, infill pattern and extruder temperature on the mechanical properties of 3D-printed biocomposite.

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.

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.

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.

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.