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This study aims to identify research trends and technological evolution in the polymer three-dimensional (3D) printing process that can effectively identify the direction of technological advancement and progress of acceptance in both society and key manufacturing industries.

The Scopus database was used to collect data on polymer 3D printing papers. This study uses bibliometric approach along with network analytic techniques to identify and discuss the most important countries and their scientific collaboration, compares income groups and analyses keyword trends.

It was found that top research production results from heavy investments in research and development. The USA has the highest number of papers among the high-income countries. However, scientific production in the other two income groups is strongly dominated by China and India. Keyword analysis shows that countries with lower incomes in certain areas, such as composite and bioprinting, have fallen behind other groups over time. International collaborations were suggested as mechanisms for those countries to catch up with the current research trends. The evolution of the research field, which started with a focus on 3D printing processes and shifted to printed part designs and their applications, was discussed. The advancement of the research topic suggests that translational research on polymer 3D printing has been led mainly by research production from higher-income countries and countries with large research and development investments.

Previous studies have conducted performance analysis, science mapping and network analysis in the field of 3D printing, but none have focused on global research trends classified by country income. This study has conducted a bibliometric analysis and compared the outputs according to various income levels according to the World Bank classification.

This research investigates how the value creation interests and activities of different stakeholder groups within one open source software (OSS) project influence the project's development over time.

The authors conducted a case study of OpenSimulator using textual and thematic analyses of the initial four years of OpenSimulator developer mailing list to identify each stakeholder group and guide our analysis of their interests and value creation activities over time.

The analysis revealed that while each stakeholder group was active within the OSS project's development, the different groups possessed complementary interests that enabled the project to evolve. In the formative period, entrepreneurs were interested in the software's strategic direction in the market, academics and SMEs in software functionality and large firms and hobbyists in software testing. Each group retained its primary interest in the maturing period with academics and SMEs separating into server- and client-side usability. The analysis shed light on how the different stakeholder groups overcame tensions amongst themselves and took specific actions to sustain the project.

The authors extend stakeholder theory by reconceptualizing the focal organization and its stakeholders for OSS projects. To date, OSS research has primarily focused on examining one project relative to its marketplace. Using stakeholder theory, we identified stakeholder groups within a single OSS project to demonstrate their distinct interests and how these interests influence their value creation activities over time. Collectively, these interests enable the project's long-term development.

This study aims to evaluate the issue of household food waste in Greece, with an emphasis on assessing the level of awareness and key behaviours among consumers. Moreover, the study focuses on examining consumer behaviours related to food waste and identifying distinct consumer profiles that can provide valuable insights into the issue in order to uncover unique behavioural factors and offer targeted interventions to curb food waste in the country.

A nationwide survey was conducted in Greece using a structured online questionnaire, which was sent to 1,270 participants, through the snowball technique. However, due to some incomplete responses, only 1,238 of the responses were considered suitable for analysis. Common descriptive statistics were used to sketch the respondents' profiles, and a non-hierarchical K-means cluster analysis was performed to identify distinct subgroups in the sample.

The study revealed a significant level of food waste awareness among Greek consumers. The cluster analysis identified four distinct consumer groups and substantial differences among them. Notably, sociodemographic analysis underscored a pronounced inclination towards food wastage among younger individuals. Additionally, each cluster's attributes, including their environmental awareness, shopping behaviours meal-planning tendencies and propensity for excess purchases, were examined. Consequently, this study underscored the imperative for targeted informational campaigns tailored for consumer segmentation, offering a pathway to identify prospective interventions conducive to the promotion of sustainable food-consumption practices.

The originality and value of this work lie in its unique focus on addressing the significant issue of household food waste within the context of Greece. What sets this study apart is the application of non-hierarchical K-means cluster analysis (which allowed the authors to identify distinct consumer profiles), a method not widely utilised in the Greek context. By filling this knowledge gap, this study offers crucial insights that can inform targeted interventions aimed at reducing food waste, in alignment with global sustainability initiatives such as the United Nations Agenda 2030 and the European Union's “Farm to Fork” strategy. Additionally, this study contributes to the efforts to provide innovative solutions to prevent household food waste and foster a sustainable future in an ever-changing international environment marked by various crises

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

This study aims to explore the role of team personality and leader’s humor style on the use of humor in group communication and the extent to which group humor mediates the association between team personality on the one hand, psychological safety, collective emotional intelligence and group satisfaction on the other hand.

The authors used a survey to collect data from 304 employees nested in 83 groups working in organizations from various sectors in Romania.

The study results show that extraversion is positively associated with group affiliative humor, while neuroticism has a positive association with group aggressive humor. The leader’s affiliative humor style had a significant positive effect on group affiliative humor, while the effect of leader’s aggressive humor style on the use of aggressive humor in groups was not significant. Furthermore, the authors examined the mediation role of group humor in the relationship between team personality and team emergent states and satisfaction. The authors found that group aggressive humor mediates the association between neuroticism and group emotional intelligence, psychological safety and satisfaction, while affiliative humor mediates the association between extraversion and emotional intelligence and team satisfaction.

The study reports one of the first attempts to explore the multilevel interplay of team personality and humor in groups as they relate to emergent states.

This paper aims to investigate whether the effects of children's current learning are related to their learning efficiency and behavior when they are exposed to two different gaming media.

In this paper the authors used a quasi-experimental design to determine whether game-based learning can be improved by using mobile devices equipped with augmented reality (AR).

The control group using the card game was careful to find the correct answer, with the intention of “obtaining the maximum score with the highest rate of correctness,” whereas the experimental group using the AR board game played aggressively by “obtaining the maximum score with the highest number.”

Although integrating an AR board game into the curriculum is an effective approach, the need to implement such a game in response to different learning attitudes and behaviors of students should be addressed.

Depending on the learning situation, different teaching methods and aids can be used to help students effectively learn. The recommendations based on this experiment can broaden the teaching field and allow for a wider range of experimental studies.

Learning behavior was observed, and user attention was interpreted using MindWave Mobile.

This study aims to use big data analysis and sheds light on key hotel features that play a role in the revisit intention of customers. In addition, this study endeavors to highlight hotel features for different customer segments.

This study uses a machine learning method and analyzes around 100,000 reviews of customers of 100 selected hotels around the world where they had indicated on Trip Advisor their intention to return to a particular hotel. The important features of the hotels are then extracted in terms of the 7Ps of the marketing mix. This study has then segmented customers intending to revisit hotels, based on the similarities in their reviews.

In total, 71 important hotel features are extracted using text analysis of comments. The most important features are the room, staff, food and accessibility. Also, customers are segmented into 15 groups, and key hotel features important for each segment are highlighted.

In this research, the number of repetitions of words was used to identify key hotel features, whereas sentence-based analysis or group analysis of adjacent words can be used.

This study highlights key hotel features that are crucial for customers’ revisit intention and identifies related market segments that can support managers in better designing their strategies and allocating their resources.

By using text mining analysis, this study identifies and classifies important hotel features that are crucial for the revisit intention of customers based on the 7Ps. Methodologically, the authors suggest a comprehensive method to describe the revisit intention of hotel customers based on customer reviews.

There is limited research on the behavior of different categories of space tourists as identified by different types of space tourism. To address this deficiency, the purpose of this study is to examine what factors make consumers participate in orbital and/or suborbital space tourism, along with three dimensions of motivation, constraint and artificial intelligence. To achieve this study’s goals, a comprehensive research model was developed that included three dimensions of intrinsic and extrinsic motivation, intrapersonal and interpersonal constraint and awareness of and trust in artificial intelligence, in comparing orbital and suborbital space tourism groups.

A questionnaire was carried out with respondents who wanted to participate in orbital (n = 332) and suborbital (n = 332) space tourism in the future. Partial least squares-structural equation modeling, fuzzy-set qualitative comparative analysis, multi-group analysis and deep learning were used to understand potential space tourist behavior.

Extrinsic motivation has the greatest positive impact on behavioral intention, followed by awareness of and trust in artificial intelligence, while intrapersonal constraint strongly negatively affects behavioral intention. Surprisingly, interpersonal constraint is insignificant by partial least squares-structural equation modeling but is still one of sufficient causal configurations by fuzzy-set qualitative comparative analysis. Interestingly, the two types of space tourism have very distinct characteristics.

This study created a comprehensive integrated research model with three dimensions of motivation, constraint and artificial intelligence, along with potential orbital and suborbital space tourist groups, to identify future consumer behavior. Importantly, this study used multi-analysis methods using four different approaches to better shed light on potential orbital and suborbital space tourists.

对不同类型太空旅游所识别的不同类别太空游客行为的研究有限。 为了解决这一缺陷, 这项工作研究了哪些因素使消费者参与轨道和/或亚轨道太空旅游, 以及动机、约束和人工智能三个维度。 为了实现研究目标, 在比较轨道和亚轨道太空旅游群体时, 开发了一个综合研究模型, 包括内在和外在动机、内在和人际约束以及对人工智能的认识和信任三个维度。

对希望在未来参与轨道 (n = 332) 和亚轨道 (n = 332) 太空旅游的受访者进行了问卷调查。 利用偏最小二乘法 (PLS)-结构方程模型 (SEM)、模糊集定性比较分析 (fsQCA)、多组分析和深度学习来了解潜在的太空游客行为。

外在动机对行为意图的积极影响最大, 其次是对人工智能的认识和信任, 而内在约束对行为意图有强烈的负面影响。 令人惊讶的是, 人际约束对于 PLS-SEM 来说是微不足道的, 但对于 fsQCA 来说仍然是充分的因果配置之一。 有趣的是, 这两类太空旅游具有非常鲜明的特点。

这项工作创建了一个全面的综合研究模型, 具有动机、约束和人工智能三个维度, 以及潜在的轨道和亚轨道太空旅游群体, 以确定未来的消费者行为。 重要的是, 这项研究采用了多种分析方法, 使用四种不同的方法来更好地揭示潜在的轨道和亚轨道太空游客。

existe una investigación limitada sobre el comportamiento de las diferentes categorías de turistas espaciales identificados por diferentes tipos de turismo espacial. Para abordar esta deficiencia, este trabajo examina qué factores hacen que los consumidores participen en el turismo espacial orbital y/o suborbital, junto con tres dimensiones de motivación, restricción e inteligencia artificial. Para lograr los objetivos del estudio, se desarrolló un modelo de investigación integral que incluía tres dimensiones de motivación intrínseca y extrínseca, restricción intrapersonal e interpersonal, y conocimiento y confianza en la inteligencia artificial, al comparar grupos de turismo espacial orbital y suborbital.

se realizó un cuestionario con los encuestados que querían participar en el turismo espacial orbital (n = 332) y suborbital (n = 332) en el futuro. Se utilizaron modelos de ecuaciones estructurales (SEM) de mínimos cuadrados parciales (PLS), análisis comparativo cualitativo de conjuntos borrosos (fsQCA), análisis multigrupo y aprendizaje profundo para comprender el comportamiento potencial del turista espacial.

la motivación extrínseca tiene el mayor impacto positivo en la intención de comportamiento, seguida de la conciencia y la confianza en la inteligencia artificial, mientras que la restricción intrapersonal afecta negativamente la intención de comportamiento. Sorprendentemente, la restricción interpersonal es insignificante por PLS-SEM, pero sigue siendo una de las configuraciones causales suficientes por fsQCA. Curiosamente, los dos tipos de turismo espacial tienen características muy distintas.

este trabajo creó un modelo de investigación integral integral con tres dimensiones de motivación, restricción e inteligencia artificial, junto con posibles grupos de turistas espaciales orbitales y suborbitales para identificar el comportamiento futuro del consumidor. Es importante destacar que este estudio empleó métodos de análisis múltiple utilizando cuatro enfoques diferentes para arrojar mejor luz sobre los posibles turistas espaciales orbitales y suborbitales.

Entrepreneurial intention (EI) has been studied prolifically, as a precursor to entrepreneurial action, and a desired outcome of entrepreneurship education. Yet, the paucity of extant studies that analyze its temporal stability has been noted. This paper aims to address this gap by studying the temporal stability of EI, investigating its persistence as an attitudinal state over time.

A series of intraindividual and group-level longitudinal analyses were undertaken, over an 11-year period, using a student sample from Portugal. The authors highlight the magnitude of EI change over time, where item-structure, relative and absolute stability and group and individual-level EI changes are all considered.

Results indicate an initially strong to moderate EI item-structure stability and relative stability over the first five years, with moderate signs of deterioration. This deterioration becomes even more pronounced across the full 11-year period. Regarding EI absolute stability, while college students (as a group) did not display a general tendency to develop higher or lower EI during the first five years, a small deterioration was found over the 11-year period. At the individual level, EI instability was detected, and this increased with time. Finally, the exploratory results suggest that entrepreneurship education may buffer the deterioration of EI.

The findings provide a more nuanced reasoning for dampened EI–entrepreneurial behavior associations and highlight key determinants of EI change, which can inform educational experts and policymakers.

The legitimacy of the EI field lays heavily on the existence of a stable EI construct and a strong relationship between intentions and behavior. The methodology provides a new and more complete picture of EI’s temporal stability.

This review paper seeks to enhance knowledge of how pre-loading affects reinforced concrete (RC) beams under fire. It investigates key factors like deflection and load capacity to understand pre-loading's role in replicating RC beams' actual responses to fire, aiming to improve fire testing protocols and structural fire engineering design.

This review systematically aggregates data from existing literature on the fire response of RC beams, comparing scenarios with (WP) and without pre-loading (WOP). Through statistical tools like the two-tailed t-test and Mann–Whitney U-test, it assesses deflection extremes. The study further examines structural responses, including flexural and shear behavior, ultimate load capacity, post-yield behavior, stiffness degradation and failure modes. The approach concludes with a statistical forecast of ideal pre-load levels to elevate experimental precision and enhance fire safety standards.

The review concludes that pre-loading profoundly affects the fire response of RC beams, suggesting a 35%–65% structural capacity range for realistic simulations. The review also recommended the initial crack load as an alternative metric for determining the pre-loading impact. Crucially, it highlights that pre-loading not only influences the fire response but also significantly alters the overall structural behavior of the RC beams.

The review advances structural fire engineering with an in-depth analysis of pre-loading's impact on RC beams during fire exposure, establishing a validated pre-load range through thorough statistical analysis and examination of previous research. It refines experimental methodologies and structural design accuracy, ultimately bolstering fire safety protocols.