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The study is aimed to compare the prediction success of basic machine learning and ensemble machine learning models and accordingly create novel prediction models by combining machine learning models to increase the prediction success in construction labor productivity prediction models.

Categorical and numerical data used in prediction models in many studies in the literature for the prediction of construction labor productivity were made ready for analysis by preprocessing. The Python programming language was used to develop machine learning models. As a result of many variation trials, the models were combined and the proposed novel voting and stacking meta-ensemble machine learning models were constituted. Finally, the models were compared to Target and Taylor diagram.

Meta-ensemble models have been developed for labor productivity prediction by combining machine learning models. Voting ensemble by combining et, gbm, xgboost, lightgbm, catboost and mlp models and stacking ensemble by combining et, gbm, xgboost, catboost and mlp models were created and finally the Et model as meta-learner was selected. Considering the prediction success, it has been determined that the voting and stacking meta-ensemble algorithms have higher prediction success than other machine learning algorithms. Model evaluation metrics, namely MAE, MSE, RMSE and R², were selected to measure the prediction success. For the voting meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0499, 0.0045, 0.0671 and 0.7886, respectively. For the stacking meta-ensemble algorithm, the values of the model evaluation metrics MAE, MSE, RMSE and R² are 0.0469, 0.0043, 0.0658 and 0.7967, respectively.

The study shows the comparison between machine learning algorithms and created novel meta-ensemble machine learning algorithms to predict the labor productivity of construction formwork activity. The practitioners and project planners can use this model as reliable and accurate tool for predicting the labor productivity of construction formwork activity prior to construction planning.

The study provides insight into the application of ensemble machine learning algorithms in predicting construction labor productivity. Additionally, novel meta-ensemble algorithms have been used and proposed. Therefore, it is hoped that predicting the labor productivity of construction formwork activity with high accuracy will make a great contribution to construction project management.

Particles are of the controlled release delivery systems. Also, topically applied olive oil has a protective effect against ultraviolet B (UVB) exposure. Due to its sensitivity to oxidation, various studies have investigated the production of olive oil particles. The purpose of this study was to use chitosan and sodium alginate as the vehicle polymers for olive oil.

The gelation method used to prepare the sodium alginate miliparticles containing olive oil and particles were coated with chitosan. Morphology and size, zeta potential, infrared spectrum of olive oil miliparticles, encapsulation efficiency and oil release profile were investigated. Among 12 primary fabricated formulations, formulations F₅ (olive oil loaded alginate miliparticles) and F₁₁ (olive oil loaded alginate miliparticles + chitosan coat) were selected for further evaluations.

The size of the miliparticles was in the range of 1,100–1,600 µm. Particles had a spherical appearance, and chitosan coat made a smoother surface according to the scanning electron microscopy. The zeta potential of miliparticles were −30 mV for F₅ and +2.7 mV for F₁₁. Fourier transform infrared analysis showed that there was no interaction between olive oil and other excipients. Encapsulation efficiency showed the highest value of 85% in 1:4 (olive oil:alginate solution) miliparticles in F₁₁. Release study indicated a maximum release of 68.22% for F₅ and 60.68% for F₁₁ in 24 h (p-value < 0.016). Therefore, coating with chitosan had a marked effect on slowing the release of olive oil. These results indicated that olive oil in various amounts can be successfully encapsulated into the sodium-alginate capsules cross-linked with glutaraldehyde.

To the best of the authors’ knowledge, no study has used chitosan and sodium alginate as the vehicle polymers for microencapsulation of olive oil.

The current study aims to examine if the internal marketing and employees’ personal traits can predict their green innovative behavior at the workplace.

A survey was conducted with 683 frontline employees working in four- and five-star hotels in Serbia. Zero-order bivariate correlations among variables and linear multiple regression were conducted to predict green innovative behavior based on internal marketing, personality traits and psychological capital. Binary genetic algorithms were used to segregate the subset of predictors that would be most suitable to describe variance in the outcome.

The results showed that internal communication, incentive and reward systems, work support, work environment, openness and creative self-efficacy were the most important predictors of almost all the phases of green innovative behavior.

The research showed that a multidimensional approach in analyzing green innovative behavior is necessary as some factors can be significant or not so significant predictors. Acknowledging that innovation is a multistage process, entailing distinct activities and requiring varied individual behaviors to accomplish each task, amplifies the importance of this inquiry. Employees’ personal characteristics have direct impact on green innovative behavior in hospitality. Further, the results gave an insight into the possible mix of elements of internal marketing that can be used for boosting employees’ green innovative behavior in hospitality. This is important as implementing effective internal marketing practices empowers organizations to motivate employees to invest discretionary efforts.

本研究旨在探讨内部营销和员工个人特质是否能预测他们在工作场所的绿色创新行为。

在塞尔维亚的四星和五星级酒店中, 对683名一线员工进行了调查。在变量之间进行了零阶双变量相关性和线性多元回归, 以预测基于内部营销、个性特质和心理资本的绿色创新行为。使用二元遗传算法(GAs)将适用于描述结果变异性的预测子集进行分离。

结果显示, 内部沟通、激励和奖励制度、工作支持、工作环境、开放性和创造力自效能是几乎所有绿色创新行为阶段的最重要的预测因素。

研究表明, 分析绿色创新行为需要采用多维度的方法, 因为某些因素可能是更或更少决定性的预测因素。承认创新是一个多阶段的过程, 涉及到不同的活动, 并要求采用不同的个体行为来完成每个任务, 这加强了对这一调查的重要性。员工的个人特征直接影响了酒店业的绿色创新行为。此外, 结果揭示了可以用于促进酒店业员工绿色创新行为的内部营销元素可能的混合。这是重要的, 因为实施有效的内部营销实践使组织能够激励员工投入可自由支配的努力。

El presente estudio examina si el marketing interno y los rasgos de personalidad de los empleados pueden predecir su comportamiento innovador ecológico en el lugar de trabajo.

Se realizó una encuesta a 683 empleados de primera línea que trabajan en hoteles de cuatro y cinco estrellas en Serbia. Se llevaron a cabo correlaciones bivariadas de orden cero y regresiones lineales múltiples (LM) para predecir el comportamiento innovador ecológico en función del marketing interno, los rasgos de personalidad y el capital psicológico. Se utilizaron algoritmos genéticos binarios (AGs) para segregar el subconjunto de predictores más adecuado para describir la variabilidad en el resultado.

Los resultados mostraron que la comunicación interna, los sistemas de incentivos y recompensas, el apoyo en el trabajo, el entorno laboral, la apertura y la autoeficacia creativa eran los predictores más importantes en casi todas las fases del comportamiento innovador ecológico.

La investigación demostró que es necesario un enfoque multidimensional para analizar el comportamiento innovador ecológico, ya que algunos factores pueden o no ser predictores significativos. Reconocer que la innovación es un proceso de múltiples etapas, que implica actividades distintas y requiere comportamientos individuales variados para realizar cada tarea, amplifica la importancia de esta investigación. Las características personales de los empleados influyen directamente en el comportamiento innovador ecológico en la industria hotelera. Además, los resultados ofrecen una visión de la posible combinación de elementos de marketing interno que se pueden utilizar para impulsar el comportamiento innovador ecológico de los empleados en la hotelería. Esto es importante ya que la implementación de prácticas eficaces de marketing interno permite a las organizaciones motivar a los empleados para que inviertan esfuerzos discrecionales.

This paper investigates the relationship between the sustainable finance disclosure regulation (SFDR) and the performance of unlisted real estate funds.

While existing literature has primarily focused on the impact of voluntary sustainability disclosure, such as certifications or reporting standards, this study addresses a significant research gap by constructing and analyzing the financial J-Curve of 40 funds under the SFDR. The authors employ a panel regression analysis to examine the effects of different SFDR categories on fund performance.

The findings reveal that funds categorized under Article 8 of the SFDR do not exhibit significantly poorer performance compared to funds categorized under Article 6 during the initial phase after launch. On average, Article 8 funds even demonstrate positive returns earlier than their peers. However, the panel regression analysis suggests that Article 8 funds slightly underperform when compared to Article 6 funds over time.

While investors may not anticipate lower initial returns when opting for higher SFDR categories, they should nevertheless be aware of the limitations inherent in the existing SFDR labeling system within the unlisted real estate sector.

To the best of our knowledge, this study represents the first quantitative examination of unlisted real estate fund performance under the SFDR. By providing unique insights into the J-Curves of funds, our research contributes to the existing body of knowledge on the impact of sustainability regulations in the financial sector.

This study aims to develop a self-reflection scale useful for teachers to improve their skills and to clarify the Japanese teachers’ characteristics during mathematics lesson observation (MLO). In MLO, it is important to understand the lesson plan in advance to clarify observation points, and we aim to develop a scale including these points.

Based on the pre-questionnaire survey, nine perspectives and two situations for MLO were extracted. From these, a questionnaire for MLO was created. The results obtained from 161 teachers were examined, and exploratory factor analysis was conducted. ANOVA was conducted to analyze the effect of differences across the duration of teaching experience on the identified factors.

We developed a self-reflection scale consisting of 14 items with three factors: [B1] focus on instructional techniques and evaluation, [B2] focus on proactive problem-solving lesson development and [B3] focus on the mathematical background of the learning content. While duration of teaching experience showed no effect, three factors of the self-reflection scale for MLO showed a significant effect. Further multiple comparisons revealed the degree of focus was [B2]>[B1]>[B3].

Teachers who use this developed scale may grasp the strengths and weaknesses of their own MLO, which leads to self-improvement. The perspectives emphasized in lesson observation are the same when creating lesson plans and implementing lessons, leading to lesson improvement. Furthermore, based on the characteristics of teachers revealed, new training programs regarding MLO can lead to higher-quality lesson studies.

The paper reviews existing literature on South Asian ecosystem-based disaster risk reduction (DRR) and identifies how community participation can be used to plan and implement ecosystem-based DRR approaches.

The literature review methodology involved several stages. Firstly, the research objective was determined. Secondly keywords for the literature search were determined. Scopus, Google Scholar, JSTOR and AUT online library were utilized for the literature search. After the search, the literature was screened. The study design, methodology, results and limitations were identified and documented. After data extraction, the literature was analyzed. The patterns, trends and inconsistencies in the literature were identified based on the research question. Later the gaps, controversies and future research needs were identified. Then, a comprehensive and structured literature review that summarizes the relevant literature, synthesizes the findings and provides a critical evaluation of the literature was documented. After writing the document, it was reviewed and edited to ensure its clarity, accuracy and coherence.

The paper identifies four different themes recurrently emerging in literature on the importance of community participation in ecosystem-based DRR in South Asia. The themes are local community participation in ecosystem-based DRR governance, knowledge production, livelihood enhancement and increased public acceptance.

The paper also illustrates the challenges in integrating community participation with the dominant physical scientific approaches ecosystem-based DRR and proposes a five-element framework to facilitate the integration.

This study presents a systematic literature review (SLR) of the interdisciplinary literature on drones in last-mile delivery (LMD) to extrapolate pertinent insights from and into the logistics management field.

Rooting their analytical categories in the LMD literature, the authors performed a deductive, theory refinement SLR on 307 interdisciplinary journal articles published during 2015–2022 to integrate this emergent phenomenon into the field.

The authors derived the potentials, challenges and solutions of drone deliveries in relation to 12 LMD criteria dispersed across four stakeholder groups: senders, receivers, regulators and societies. Relationships between these criteria were also identified.

This review contributes to logistics management by offering a current, nuanced and multifaceted discussion of drones' potential to improve the LMD process together with the challenges and solutions involved.

The authors provide logistics managers with a holistic roadmap to help them make informed decisions about adopting drones in their delivery systems. Regulators and society members also gain insights into the prospects, requirements and repercussions of drone deliveries.

This is one of the first SLRs on drone applications in LMD from a logistics management perspective.

Existing literature points out that conventional educational modes are not sufficiently motivational for students. Concurrently, the contemporary society requires awareness of sustainability within project management. The purpose of this paper is to investigate how the use of simulations in project management education can positively impact students’ awareness of sustainability and enhance their ability to navigate projects in a sustainable way.

Experiment where 26 experienced professionals with different backgrounds engaged in three extensive project management simulations with sustainable aspects and participated in pre- and post-assessments.

Our research shows that simulations have a high potential for enhancing learning on project management with sustainable aspects. We conclude that simulations can significantly contribute to enhancing student awareness of sustainability. This is through directly confronting them with three areas in which sustainability impacts project management, that is the management of environmental, social, and economic aspects; through handling opportunities, complexities, and adaptability; and by assuming responsibility for sustainable development in the simulation case.

We have shown that simulations – as a part of project management education – are highly likely to augment students' capacity to navigate their projects in a sustainable way.

This paper offers results of an empirical study on simulations as a means to create awareness of ability to navigate projects in a sustainable way. The paper provides extensive qualitative statements from participants, and thereby gives the reader insights into the raw data leading to insightful conclusions for the field of project management education.

The purpose of this paper is to analyze the life history of the Spanish Generation X over the last five decades.

Considering that the generational cohort concept can be identified from the marketing side as a market segment, this paper proposes to analyze the socio-economic and cultural context that has marked the different life stages of Generation X, and how they have related to brands according to their needs, desires and aspirations.

The results show that the customer journey can be considered a circular concept. The customer’s relationship with the brand can begin in childhood and continue into adulthood, such that the emotional relationship established with the brand as a child influences purchase decisions in adulthood.

Although limited to the analysis of Generation X and its relationship with brands, this paper shows the importance of knowing the socio-economic, legal and cultural context of a generation.

As a business implication, the importance of remarketing is evident as a business strategy that reinforces the emotional connection between the brand and the different generations.

From a social point of view, this paper shows the power of brands as an element of self-expression of the needs, tastes or preferences of individuals is evident.

This paper offers a different and innovative vision of the customer journey, taking into account the individual’s life cycle, and the way in which at each stage of life, he/she relates to brands in a different way.

Facilities management (FM) plays a key role in the performance of businesses to ensure the comfort of users and the sustainable use of natural resources over operation and maintenance. Nevertheless, reactive maintenance (RM) services are characterised by delays, waste and difficulties in prioritising services and identifying the root causes of failures; this is mostly caused by inefficient asset information and communication management. While linking building information modelling and the Internet of Things through a digital twin has demonstrated potential for improving FM practices, there is a lack of evidence regarding the process requirements involved in their implementation. This paper aims to address this challenge, as it is the first to statistically characterise RM services and processes to identify the most critical RM problems and scenarios for digital twin implementation. The statistical data analytics approach also constitutes a novel practical approach for a holistic analysis of RM occurrences.

The research strategy was based on multiple case studies, which adopted university campuses as objects for investigation. A detailed literature review of work to date and documental analysis assisted in generating data on the FM sector and RM services, where qualitative and statistical analyses were applied to approximately 300,000 individual work requests.

The work provides substantial evidence of a series of patterns across both cases that were not evidenced prior to this study: a concentration of requests within main campuses; a balanced distribution of requests per building, mechanical and electrical service categories; a predominance of low priority level services; a low rate of compliance in attending priority services; a cumulative impact on the overall picture of five problem subcategories (i.e. Building-Door, Mechanical-Plumbing, Electrical-Lighting, Mechanical-Heat/Cool/Ventilation and Electrical-Power); a predominance of problems in student accommodation facilities, circulations and offices; and a concentration of requests related to unlisted buildings. These new patterns form the basis for business cases where maintenance services and FM sectors can benefit from digital twins. It also provides a new methodological approach for assessing the impact of RM on businesses.

The findings provide new insights for owners and FM staff in determining the criticality of RM services, justifying investments and planning the digital transformation of services for a smarter provision.

This study represents a unique approach to FM and provides detailed evidence to identify novel RM patterns of critical service provision and activities within organisations for efficient digitalised data management over a building’s lifecycle.