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Task complexity is one of the significant factors that influences and is used for forecasting employee performance and determining labor cost. However, the complexity level of tasks is unstructured, dynamic and complicated to perform. This paper develops a new method for evaluating the complexity level of tasks in the production process to support production managers to control their manufacturing systems in terms of flexibility, reliability to production planning and labor cost.

The complexity level of tasks will be analyzed based on the structuralist concept. Using the structure of task, the factors that significantly affect the task complexity in an assembly line will be defined, and the complexity level of the task will be evaluated by measuring the number of task components. Using the proportional 2-tuples linguistic values, the difference between the complexity levels of tasks can be compared and described clearly.

Based on the structure of the task, three contributory factors including input factors, process-operation factors and output factors that significantly affect the task complexity in an assembly line are identified in the present study. The complexity level of the task is quantified through analyzing the details of the three factors according to two criteria and six sub-criteria within the textile case study.

The proposed approach provides a new insight about the factors that have an effect on the complexity of tasks in production and remedies some of limitations of previous methods. The combination of experts' experience and scientific knowledge will improve the accuracy in determining the complexity level of tasks.

In the apparel industry, suppliers play a significant role, directly affecting customer service levels and business profits. Integrating sustainable requirements into supplier selection not only aligns with global environmental goals but also enhances business performance, social responsibility and overall industry well-being. This study aims to design a multi-criteria model to evaluate and select the most sustainable suppliers in the fashion industry, trying to balance the conflicts in the set of sustainable development criteria.

The integration of analytic hierarchy process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) offers advantages in sustainable supplier selection within the apparel industry. The AHP plays a crucial role in engaging multiple decision-makers with conflicting criteria to reach a consensus during the decision-making process. Conversely, the TOPSIS is used to compute alternative ratings. By simultaneously determining criteria weights and incorporating stakeholder preferences, hybrid models enhance decision-making strength and overcome limitations observed in classical multi-criteria decision-making techniques.

This research identified and classified 16 critical criteria impacting the selection of apparel industry suppliers, focusing on sustainable development. The criteria were weighted, providing a robust statistical foundation for the selection model. The results indicated that the most influential criteria were staff training, production capability, flexibility and practice of recycling. The proposed sustainable supplier selection model explains to decision-makers how criteria influence supplier ranking results compared to traditional models, supporting managers in making informed and sustainable supply chain decisions through continuous updates and enhancements.

This research provides new insight into the weighted impact of factors related to sustainable supplier selection in the apparel industry. The combination of a precise process and scientific knowledge will improve the quality of sustainable supplier selection.

Kapok was well-known for its oleophilic properties, but its mechanical properties and morphology impeded it from forming suitable absorbent materials. This study aims to demonstrate the process of creating an oil-absorbent web from a blend of treated kapok and polypropylene fibers.

Kapok fibers were separated from dried fruits, then the wax was removed with an HCl solution at different concentrations. The morphological and structural changes of these fibers were investigated using scanning electron microscopy images. The blending ratios of kapok and polypropylene fibers were 60/40, 70/30 and 80/20, respectively. The fiber blends were fed to a laboratory carding machine to form a web and then consolidated using the heat press technique. The absorption behavior of the formed web was evaluated regarding oil absorption capacity and oil retention capacity according to ASTM 726.

The results showed that the HCl concentration of 1.0% (wt%) gave the highest wax removal efficiency without damaging the kapok fibers. This study found that oil absorbency is influenced by the fiber blending ratio, web tensile strength and elongation, porosity, oil type and environmental conditions. The oil-absorbency of the web can be re-used for at least 20 cycles.

This study only looked at three types of oils: diesel, kerosene and vegetable oils.

When the problem of oil spills in rivers and seas is growing and causing serious environmental and economic consequences, using physical methods to recover oil spills is the most effective solution.

This research adds to the possibility of using kapok fiber in the form of a web of non-woven fabric for practical purposes.

This paper aims to develop a decision support system for predicting the knitting production’s efficiency based on the input parameters of an order. This tool supports the operations managers to make reliable decisions of estimated delivery time, which will result in reducing waste arising from late delivery, overtime and increased labor.

The decision tree method with a set of logical IF-THEN rules is used to determine the knitting production’s efficiency. Each path of the decision tree represents a rule of the following form: “IF <Condition> THEN <Efficiency label>.” Starting with identifying and categorizing input specifications, the model is then applied to the observed data to regenerate the results of efficiency into classification instances.

The production’s efficiency is the result of the interaction between input specifications such as yarn’s component, knitting fabric specifications and machine speed. The rule base is generated through a decision tree built to classify the efficiency into five levels, including very low, low, medium, high and very high. Based on this, production managers can determine the delivery time and schedule the manufacturing planning more accurately. In this research, the correct classification instances, which is simply a ratio of the correctly predicted observations to the total ones, reach 80.17%.

This research proposes a new methodology for estimating the efficiency of weft knitting production based on a decision tree method with an application of real data. This model supports the decision-making process of the estimated delivery time.

This study proposes a serial mediation model to investigate the factors influencing hospitality and tourism (H&T) students' destination choice intentions in an experiential learning context. By extending the theory of planned behavior (TPB) model to include extended variables and the stimulus-organism-response (S-O-R) theory, this study explores pathways in which experiential learning involvement (IEL) and destination emotions (DE) interact with attitude (ATT), subjective norms (SN) and perceived behavioral control (PBC) to predict H&T students' intentions (ITT). This work also examines the moderating role of destination familiarity (DF) on relationships between destination emotions and attitudes, subjective norms and perceived behavioral control.

The conceptual model and research hypotheses were each assessed using partial least squares structural equation modeling (PLS-SEM). Paper-based surveys were used to collect data from 715 students majoring in tourism and hospitality training at schools in Vietnam and applying experiential learning activities during their training.

Research results confirm a serial mediation model wherein IEL and DE promote the formation of intention to choose a specific destination through various pathways: IEL? PBC? ITT; IEL? DE? SN/PBC? ITT and DE? SN/PBC? ITT. In addition, the results show that destination familiarity dampens the relationship between emotions and attitudes as well as with perceived behavioral control.

This study offers practical recommendations for destination management organizations (DMOs) seeking to increase the intention of H&T students to choose their particular destination. These recommendations include: forming strategic alliances with H&T educational institutions; implementing preferential policies like discounted or free admission to attractions and corporate discounts for students; launching targeted digital marketing campaigns on social media platforms and promoting the destination through youth-oriented media such as television shows and music videos.

From addressing the research gap by developing and testing a serial mediation model of destination choice intention of H&T students in an experiential learning context, this study offers new insights into developing scales of constructs in the research model associated with the context of experiential learning and highlights the importance of IEL and DE as a stimulus to achieve ATT, SN and PBC, all of which serve to increase H&T students' intention to choose a destination through different paths under the moderating lens of destination familiarly.

This research aims to investigate the relationship between value orientations, attitude, behavioral intention and usage behavior of electric ride hailing service in Vietnam through the moderating role of social influences.

In total, 552 valid questionnaires were collected using the survey questionnaire to analyze the effects of value orientations on attitude toward electric ride hailing and behavioral intention. Besides, this study investigates the mediating effect of attitude toward electric ride hailing and behavioral intention in the relationship with the behavior usage of electric ride hailing service as well as the moderating effect of social influence.

Three types of value orientations stimulate attitude toward electric ride hailing service and behavioral intention. In addition, attitude toward electric ride hailing service and behavioral intention play the mediating role in the effect of value orientations on behavior usage. Specifically, social influence plays a negative moderating role in the linkage between attitude towards and behavioral intention of electric ride hailing service and between intention and the behavior usage.

While numerous studies have investigated the influence of customers’ value orientations on multiple green purchase behaviors, this study offers new insights into the moderating role of social influence in the linkages between attitude, behavioral intention and usage behavior of electric ride hailing service.