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Collective political traumas emerge from human behavior as a result of political motivation. These events include destructive and intense violence that disrupt the biopsychosocial processes of people in general. A study was conducted on individuals involved in the conflict between the Kurds in southeastern Turkey and security forces. This study aims to determine whether perceived social support, assumptions about the world, psychological resilience and psychological symptoms predict post-traumatic growth (PTG). In addition, the study examines whether differences existed between the participants’ PTG and the four cited variables according to the type of trauma and major sociodemographic variables.

This study recruited 324 individuals who completed the PTG Inventory, Multidimensional Perceived Social Support Scale, World Assumption Scale, Brief Psychological Resilience Scale and Symptom Checklist-90-R Symptom Screening List. Data were analyzed using regression analysis, ANOVA and t-test for independent groups.

Analysis revealed that assumptions about the world, perceived social support and level of psychological symptoms significantly predicted PTG level. The level of psychological symptoms was significantly higher among individuals exposed than those not exposed to prison. Moreover, participants with low levels of education and income displayed low levels of social support and psychological resilience but high levels of psychological symptoms. When working with victims of collective political trauma, the fact that people who are exposed to prison and torture experience and those with low socioeconomic levels pose serious risks in terms of psychological problems must be considered.

This research is important because it collects data on the effects of collective political traumas.

This study establishes the principles and process steps of a new basic trousers pattern using measurements obtained according to the rules of the anthropometric measurement system. The newly developed pattern-making system in this study will be called the “Anthropometric Measurements Based Pattern Making System” (AnMePa). It is aimed at producing trousers that are more fitting to the body, thanks to this pattern-making system.

In this research, four pattern-making systems used in many parts of the world were compared with the “Anthropometric Measurements Based Pattern Making System” (AnMePa) with regard to the overall appearance and body fit of trousers prepared according to these systems. 10 virtual mannequins (VM) with different adult female body measurements were created, and trousers patterns were prepared for these mannequins. The trousers’ patterns were made and dressed on the mannequins in a 3D virtual dressing system. The body fit of the virtual garments was evaluated by five experts. The scores given by the experts were evaluated using the fuzzy logic method.

According to the results, it is seen that the new basic trousers pattern developed by utilizing the anthropometric measurement system, AnMePa, provides the best body fit among the basic trousers patterns created according to the other examined pattern-making systems. The combination of 3D virtual dressing and fuzzy logic in the evaluation of garment body fit is considered an innovative method for the future of fashion design and production.

In the developed AnMePa, unlike the existing pattern-making systems, values that can be associated with the body measurements of individuals in a way that could be suitable for each community were used instead of constant values in the pattern-making process. Furthermore, the integration of 3D virtual fitting and fuzzy logic in assessing garment fit is considered a pioneering approach with significant implications for the future landscape of fashion design and production.

Acknowledging, on the one hand, the increasing fragility of supply chains and the number of risks involved in supply chain operations and, on the other hand, the role of small- and medium-sized enterprises (SMEs) in supply chains and the high exposure of these firms to risks of different types, this study aims to examine the relationship between supply chain risk management (SCRM) and innovation performance in SMEs. Furthermore, the impact of technological turbulence on this relationship was studied to take into account recent technological changes.

Structural equation modelling was carried out on a sample of Turkish SMEs to test the hypotheses developed.

The findings presented allow the authors to better understand the link between SCRM and innovation performance in SMEs. More precisely, empirical evidence is provided about the impact of SCRM components such as maturity and ability on innovation performance. Furthermore, the findings show the impact of technological turbulence on both SCRM and innovation performance.

By focusing on SCRM in SMEs, this paper contributes to the body of knowledge with regard to SCRM in general and with regard to SMEs in particular; research on the latter has only started recently. Moreover, by having studied SMEs from a developing country (other than China), this paper helps to develop a broader and more diverse perspective of SCRM.

This study was conducted to address the methodical shortcomings and high associated cost of understanding the use of new, poorly understood architectural spaces, such as makerspaces. The proposed quantified method of enhancing current post-occupancy evaluation (POE) practices aims to provide architects, engineers and building professionals with accessible and intuitive data that can be used to conduct comparative studies of spatial changes, understand changes over time (such as those resulting from COVID-19) and verify design intentions after construction through a quantified post-occupancy evaluation.

In this study, we demonstrate the use of ultra-wideband (UWB) technology to gather, analyze and visualize quantified data showing interactions between people, spaces and objects. The experiment was conducted in a makerspace over a four-day hackathon event with a team of four actively tracked participants.

The study shows that by moving beyond simply counting people in a space, a more nuanced pattern of interactions can be discovered, documented and analyzed. The ability to automatically visualize findings intuitively in 3D aids architects and visual thinkers to easily grasp the essence of interactions with minimal effort.

By providing a method for better understanding the spatial and temporal interactions between people, objects and spaces, our approach provides valuable feedback in POE. Specifically, our approach aids practitioners in comparing spaces, verifying design intent and speeding up knowledge building when developing new architectural spaces, such as makerspaces.

In this study the authors aim to comprehensively investigate the determinants of voting behavior in Turkey, with a specific focus on the dynamics of the center-periphery debate. Mainly, the authors focus on regional voting patterns during the period that is dominated by the Justice and Development Party (JDP/AKP) in the elections. The authors apply the random effects generalized least squares (GLS) methodology, and analyze electoral data covering four pivotal parliamentary elections (2007, 2011, 2015 and 2018) across all 81 provinces (NUTS III regions). The authors individually examine voting dynamics of the four major parties in parliament: the JDP/AKP, the Republican People's Party (RPP/CHP), the Nationalist Movement Party (NMP/MHP) and the Peoples' Democratic Party (PDP/HDP). The authors contribute to a comprehensive understanding of how socioeconomic cleavages, economic performance, party alignment and social dynamics shape voter preferences in the Turkish context, thereby addressing gaps in the existing literature.

This research employs an ecological study of Turkish NUTS III sub-regions, covering national elections from 2007 to 2018. The authors utilize the random effects GLS method to account for heteroscedasticity and time effects. The inclusion of the June and November 2015 elections enables a comprehensive analysis of the evolving dynamics in Turkish voting behavior. The results remain robust when applying pooled OLS and fixed effect OLS techniques for control.

The study's findings reveal that economic performance, specifically economic growth, plays a pivotal role in the sustained dominance of the JDP/AKP party. Voters closely associate JDP preference with economic growth, resulting in higher voting shares during periods of economic prosperity. Along with economic growth; share of agriculture in regions' GDP, female illiteracy rate, old population rate, net domestic migration, terrorism and party alignment are also influential factors in the Turkish case. Furthermore, differences among sociocultural groups, and East–West dichotomy seem to be important factors that reveal the impact of social cleavages to understand electoral choice in Turkey.

This study contributes to the existing literature by offering a comprehensive multidimensional analysis of electoral behavior in Turkey, focusing on the JDP/AKP dominance period. The main contribution of this study is its multidimensional perspective on the power bases of all main parties, considering key voter choice theories (cleavages, party alignment and retrospective economic performance voting) that have not been systematically analyzed in prior research. The main research question of this study is to examine which factors affect voting behavior in Turkey and how the dynamics of center-periphery or eastern-western region voting behavior under the JDP hegemony can be explained. The contribution of this study consists not only in its empirical testing of panel data approaches but also in its comprehensive analysis of four major political parties. Building upon existing studies in the literature, this research seeks to extend the understanding of voting dynamics for the four main parties in the parliament — JDP/AKP, RPP/CHP, NMP/MHP and PPDP/HDP — by delving into their dynamics individually, thereby expanding the scope of previous studies. This study aims to make a contribution by not only empirically testing panel data approaches but also conducting a comprehensive analysis of four major political parties. Furthermore, the separate inclusion of the 2015 elections and utilization of a panel data approach enrich the analysis by capturing the evolving dynamics of Turkish voting behavior. The study underscores the significance of socioeconomic factors, economic performance and social cleavages for voters' choices within the context of a dominant party rule.

The textile industry harms the environment at every stage of production, from the acquisition of raw materials to the disposal of finished products. It is very important for the textile industry to adapt to the basic policies on environmental sensitivity and sustainability to keep up with the transformation in production processes and the rapid changes occurring around the world in order to exist in global competition. Within the scope of sustainable development goals, it is of great importance to measure and evaluate indicators of all processes of the sector. This paper aims to present application of multi-criteria decision making (MCDM) methods for the assessment of sustainable development in textile industry.

The data of a multinational clothing company’s four-year sustainability performance between 2018 and 2021 were evaluated under 22 sustainability parameters determined using two new MCDM techniques, namely the combined consensus solution method and multi-attribute ideal real comparative analysis. In determining the criteria, priority key indicators were determined by taking into account the sector’s relationship with the environment, raw material consumption and social adequacy.

According to the application results of both methods, the year 2021 shows the best performance. It has been seen that the sustainability performance of the Inditex group has increased over the years and the results of the applied models support each other. It can be suggested that the proposed approach be applied to evaluate the progress in the textile sector with the relevant data on a particular company or on a macro scale.

This study makes an important contribution to the field in terms of the fact that the methods used are recent and have no application in the field of textiles. It allows the evaluation of different sustainability criteria together using a single method. It is very important to share data on sustainability indicators with customers, employees, suppliers, investors, partner organizations and society and evaluate performance. Analyzing sustainability performance on the basis of annual reports is important in terms of identifying good practices, sharing them with the community and setting an example. In addition, using scientific methods in the evaluation of the sustainability report data published by companies regularly provides significant feedback for policymakers and academics.

Fabrics, which are one of the raw materials of the clothing industry, constitute approximately 40–45% of the total cost of an apparel product. Due to the labor-intensive nature of this industry and failure to apply scientific methods along with the manufacturing processes, the wastes in the raw materials, including fabrics, become higher. Besides, quality deficiencies are encountered due to the same reasons. This study aims to determine the optimum total fabric layer height based on the fabric type during the cutting process with a straight knife cutting machine, which provided a decrease in the cutting errors.

Frequently used fabric types in an enterprise operating in organic cotton knitwear were listed. During the cutting tests, the straight knife cutting machine was used as the cutting device. The weight and thickness values of the fabrics were obtained to provide a comparison basis. Two different algorithms were created to evaluate the defective pieces according to fabric type, cutting height and error placement. Cutting resistances of these fabrics were also determined to evaluate the defect reasons. In the end, optimum total fabric layer count and total cutting height suggestions were proposed for each fabric type for a minimum cutting error.

At the end of this study, the error-free layers were identified per fabric type. At the same time, the optimum cutting height was suggested for each fabric basis. For 40/1 single jersey fabrics, the cutting height should be between 2.10 cm and 10.40 cm; for 30/1 single jersey fabrics, between 1.65 cm and 5.70 cm; for 20/1 single jersey fabrics, between 1.83 cm and 6.70 cm; for two-thread fleece fabrics, between 2.13 cm and 4.70 cm; and for three-thread fleece fabrics, between 0 cm and 4.90 cm.

Within the scope of the study, since the products made of knitted fabric were produced more frequently and in large quantities, the study was carried out with 15 different types of knitted fabrics at 10 different layers. The same methods should be applied for woven, denim and nonwoven fabric types, which would shed light on the following studies.

Due to scarce research carried out on the cutting procedure of the clothing industry in regards to sustainability, this study aims to contribute to this area. The main difference between this study and the studies that mostly make mathematical predictions about the cutting procedure is that it is practice-oriented.

This study aims to test the effect of individual audit quality on career advancement the audit labour market.

This paper uses data on auditors from two collapsed audit firms in China, namely, Ruihua and Zhengzhong Zhujiang, and tests the effect of individual audit quality on career advancement in the audit labour market.

The baseline results show that high-quality audits promote auditors’ career advancement. Our results hold after a battery of robustness tests. Further analyses support our hypothesis, indicating that client retention and audit fees are positively related to auditors’ prior audit quality. The effect of audit quality on career advancement does not hold for auditors from sanctioned branch offices or for auditors with prior culpable clients, as shared reputation damage can weaken the effect of high audit quality. Furthermore, this paper investigates whether the reputation enhancement effect of high audit quality can be strengthened by auditor experience, the title of “senior auditor” and IPO auditing experience. We also show that clients and audit firms place more weight on the quality of audits conducted by auditors in competitive markets and auditors with engagements matched with industries.

Together, these findings indicate the vital role of individual audit quality in auditors’ career development in the audit labour market, consistent with the reputation rationale for audit quality.

This research paper aims to investigate the effects of internal audit’s (IA) use of data analytics and the performance of consulting activities on perceived IA quality.

The authors conduct a 2 × 2 between-subjects experiment among upper and middle managers where the use of data analytics and the performance of consulting activities by internal auditors are manipulated.

Results highlight the importance of internal auditor use of data analytics and performance of consulting activities to improve perceived IA quality. First, managers perceive internal auditors as more competent when the auditors use data analytics. Second, managers perceive internal auditors’ recommendations as more relevant when the auditors perform consulting activities. Finally, managers perceive an improvement in the quality of relationships with internal auditors when auditors perform consulting activities, which is strengthened when internal auditors combine the use of data analytics and the performance of consulting activities.

From a theoretical perspective, this research builds on the IA quality framework by considering digitalization as a contextual factor. This research focused on the perceptions of one major stakeholder of the IA function: senior management. Future research should investigate the perceptions of other stakeholders and other contextual factors.

This research suggests that internal auditors should prioritize the development of the consulting role in their function and develop their digital expertise, especially expertise in data analytics, to improve perceived IA quality.

This research tests the impacts of the use of data analytics and the performance of consulting activities on perceived IA quality holistically, by testing Trotman and Duncan’s (2018) framework using an experiment.

This study aims to examine the extent to which external auditors (EAs) use the work of the internal audit function (IAF) based on the purpose of its primary activities. The authors rely on attribution theory, which suggests that individuals search for meaning when an event occurs. In this setting, the authors explore how the overall (assurance vs advisory) or specific (e.g. risk management and evaluating internal controls) focus of IAF activities influences perceived EA reliance on the IAF’s work.

The authors first explore the research question with data extracted from a broad, longitudinal survey conducted triennially by the national chapters of the Institute of Internal Auditors in Austria, Germany and Switzerland. The data includes responses from 2014, 2017 and 2020 administrations of the survey. The authors conduct a parallel survey with practicing EAs attending two training sessions of a European office of a global network firm. Hypotheses were tested using ordered logistic regression.

Among the chief audit executive (CAE) participants, the authors observe that a balanced or primarily assurance-related purpose of the IAF, relative to a primarily advisory-related purpose, is associated with higher perceived EA reliance. The authors observe similar perceptions of the extent of reliance among the EA participants.

With a unique data set of practicing internal auditors from three countries, coupled with a sample of EAs, to the best of the authors’ knowledge, this study is the first to examine differences in EA reliance across the IAF’s primary roles. The study relies on data from three European countries, which differs from prior EA reliance literature with a largely North American focus. Further, comparison between perceptions of EAs and CAEs is a novel approach and this paper’s findings suggest that perceptions of CAEs could be a reliable proxy for EA-intended behavior.