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This paper explores Lean Six Sigma principles and the DMAIC (define, measure, analyze, improve, control) methodology to propose a new Lean Six Sigma 4.0 (LSS 4.0) framework for employee occupational exams and address the real-world issue of high-variability exams that may arise.

This study uses mixed methods, combining qualitative and quantitative data collection. A detailed case study assesses the impact of LSS interventions on the exam management process and tests the applicability of the proposed LSS 4.0 framework for employee occupational exams.

The results reveal that changing the health service supplier in the explored organization caused a substantial raise in occupational exams, leading to increased costs. By using syntactic interoperability, lean, six sigma and DMAIC approaches, improvements were identified, addressing process deviations and information requirements. Implementing corrective actions improved the exam process, reducing the number of exams and associated expenses.

It is important to acknowledge certain limitations, such as the specific context of the case study and the exclusion of certain exam categories.

The practical implications of this research are substantial, providing organizations with valuable managerial insights into improving efficiency, reducing costs and ensuring regulatory compliance while managing occupational exams.

This study fills a research gap by applying LSS 4.0 to occupational exam management, offering a practical framework for organizations. It contributes to the existing knowledge base by addressing a relatively novel context and providing a detailed roadmap for process optimization.

Drawing on social exchange theory, this study clarifies the roles of authenticity, interactivity and involvement in predicting customer engagement (CE) and, ultimately, customer loyalty towards an online peer-to-peer accommodation platform. In addition, the study explores the effect of interactivity in increasing authenticity.

Data were collected through an online questionnaire of a sample of Italian tourists who had previously booked a service on Airbnb. The analyses were conducted by adopting partial least squares structural equation modelling.

The model has high power in predicting customer loyalty to an online peer-to-peer accommodation platform. Specifically, involvement is the primary predictor of CE and customer loyalty. Authenticity and interactivity also have a significant and positive effect both on CE and customer loyalty. In addition, CE partially mediates the relationship between authenticity, interactivity and involvement and customer loyalty. Finally, interactivity has a significant positive effect on authenticity.

The results encourage hospitality service providers to invest in the creation (and co-creation) of authentic experiences to increase CE and customer loyalty. Hospitality managers can also enhance CE by increasing involvement and interaction with customers through various touchpoints (online and offline) in different moments of the customer journey.

This study proposes an original model to predict customer loyalty to peer-to-peer hospitality platforms. The findings shed new light on the drivers of CE and provide empirical support for the mediating effect of CE. The study also contributes to the literature on authenticity by demonstrating the positive effect of interactivity on authenticity.

This study explores the variance in investor responses to the corporate social responsibility (CSR) performance of firms, as influenced by information sources and investor types.

This study applies a short-term event study and cross-sectional analysis with unique CSR datasets obtained from newspaper articles and the Dow Jones Sustainability Index.

Investor reactions are significantly shaped by their sources of information. Individual investors are found to predominantly respond to accessible news announcements, whereas institutional investors show heightened sensitivity to adverse news from both scrutinized sources. Foreign investors, mirroring institutional investors' patterns, uniquely react positively to index additions.

Investors’ assessment of CSR activities varies due to the differing sources of information obtained; further, it is affected by the type of investor.

The findings guide public relation managers in strategizing CSR communication toward diverse investor types. This includes recommending targeted approaches for Japanese individual investors through newspapers and TV, exercising caution in disseminating adverse news to Japanese institutions, and promoting and justifying CSR actions to foreign investors. It underscores the need for a strategic investor relations frameworks that considers accessibility, literacy, and investors' interests.

This study examines the relationship between sources of information for CSR activities and investors’ responses, an area under-represented in the literature. The author uses CSR announcement data, collected from newspapers to make the results more accurate and relevant.

This study aims to understand the mechanisms driving individuals to utilise and engage in edu-business and contribute to the industry’s development, even when they face criticism. To that end, this paper, focussing on corporate stories, explores the cultural strategies education companies employ to expand their businesses overseas.

This paper examines the case study of Kumon Institute of Education, a key player in the Japanese edu-business sector. The analysis is based on interviews conducted between 2019 and 2021 with four public relations officers who possess extensive knowledge of the company’s history. Additionally, it draws on government and company documents, as well as newspaper articles. The analysis focusses on the narrative isomorphism between the company and the government from the provider’s perspective.

Kumon’s corporate stories and narratives have been shaped by the history, culture and policies of Japan, its country of origin, rather than adopting a bottom-up approach or embracing neoliberal values. As the company expanded its international reach, its Japanese identity became a cornerstone of its narrative, heightening the appeal of its stories through the use of expert discourse and historic cultural resources. Recently, a synergy has developed between the public and private sectors in the realm of education export, reinforcing the distinctly Japanese nature of the company, which is particularly appealing to both users and employees.

This paper focusses on the edu-business itself, analysing cultural strategies that go beyond the functional aspects of management or services to understand how edu-businesses have attracted people.

The present study is performed to identify the propagation mechanism of the ripple effect as well as examine the simultaneous impact of risks on supply chain (SC) performance.

A theoretical framework with many hypotheses regarding the relationships between SC risk types and performance is established. The data are collected from a large-scale survey supported by a project of the Japanese government to promote sustainable socioeconomic development for the Association of Southeast Asian Nations (ASEAN) region, with the participation of 207 firms. Structural equation modeling (SEM) is used to test the hypotheses of the theoretical framework.

It is indicated that human-made risk causes operational risk, while natural risk causes both supply risk and operational risk. Furthermore, the impacts of human-made risk and natural risk on performance are amplified through operational risk.

This study is one of the first attempts that identifies the propagation mechanism of the ripple effect and examines the simultaneous impact of risks on performance in construction SCs.

Although many studies on risk management in construction SCs have been carried out, they mainly focus on risk identification or quantification of risk impact. It is observed that research on the ripple effect of disruptions has been very scarce.

Despite its devastating nature, the COVID-19 pandemic has also catalyzed a substantial surge in the adoption and integration of technological tools within economies, exerting a profound influence on the dissemination of information among participants in stock markets. Consequently, this present study delves into the ramifications of post-pandemic dynamics on stock market behavior. It also examines the relationship between investors' sentiments, underlying behavioral drivers and their collective impact on global stock markets.

Drawing upon data spanning from 2012 to 2023 and encompassing major world indices classified by Morgan Stanley Capital International’s (MSCI) market and regional taxonomy, this study employs a threshold regression model. This model effectively distinguishes the thresholds within these influential factors. To evaluate the statistical significance of variances across these thresholds, a Wald coefficient analysis was applied.

The empirical results highlighted the substantive role that investors' sentiments and behavioral determinants play in shaping the predictability of returns on a global scale. However, their influence on developed economies and the continents of America appears comparatively lower compared with the Asia–Pacific markets. Similarly, the regions characterized by a more pronounced influence of behavioral factors seem to reduce their reliance on these factors in the post-pandemic landscape and vice versa. Interestingly, the post COVID-19 technological advancements also appear to exert a lesser impact on developed nations.

This study pioneers the investigation of these contextual dissimilarities, thereby charting new avenues for subsequent research studies. These insights shed valuable light on the contextualized nexus between technology, societal dynamics, behavioral biases and their collective impact on stock markets. Furthermore, the study's revelations offer a unique vantage point for addressing market inefficiencies by pinpointing the pivotal factors driving such behavioral patterns.

This study aims to predict overnight stays in Italy at tourist accommodation facilities through a nonlinear, single factor, stochastic model called CIR#. The contribution of this study is twofold: in terms of forecast accuracy and in terms of parsimony (both from the perspective of the data and the complexity of the modeling), especially when a regular pattern in the time series is disrupted. This study shows that the CIR# not only performs better than the considered baseline models but also has a much lower error than other additional models or approaches reported in the literature.

Typically, tourism demand tends to follow regular trends, such as low and high seasons on a quarterly/monthly level and weekends and holidays on a daily level. The data set consists of nights spent in Italy at tourist accommodation establishments as collected on a monthly basis by Eurostat before and during the COVID-19 pandemic breaking regular patterns.

Traditional tourism demand forecasting models may face challenges when massive amounts of search intensity indices are adopted as tourism demand indicators. In addition, given the importance of accurate forecasts, many studies have proposed novel hybrid models or used various combinations of methods. Thus, although there are clear benefits in adopting more complex approaches, the risk is that of dealing with unwieldy models. To demonstrate how this approach can be fruitfully extended to tourism, the accuracy of the CIR# is tested by using standard metrics such as root mean squared errors, mean absolute errors, mean absolute percentage error or average relative mean squared error.

The CIR# model is notably simpler than other models found in literature and does not rely on black box techniques such as those used in neural network (NN) or data science-based models. The carried analysis suggests that the CIR# model outperforms other reference predictions in terms of statistical significance of the error.

The proposed model stands out for being a viable option to the Holt–Winters (HW) model, particularly when dealing with irregular data.

The proposed model has demonstrated superiority even when compared to other models in the literature, and it can be especially useful for tourism stakeholders when making decisions in the presence of disruptions in data patterns.

The novelty lies in the fact that the proposed model is a valid alternative to the HW, especially when the data are not regular. In addition, compared to many existing models in the literature, the CIR# model is notably simpler and more transparent, avoiding the “black box” nature of NN and data science-based models.

一般来说, 旅游需求往往遵循规律的趋势, 例如季度/月的淡季和旺季, 以及日常的周末和假期。该数据集包括欧盟统计局在打破常规模式的2019冠状病毒病大流行之前和期间每月收集的在意大利旅游住宿设施度过的夜晚。

本研究旨在通过一个名为cir#的非线性单因素随机模型来预测意大利游客住宿设施的过夜住宿情况。这项研究的贡献是双重的:在预测准确性方面和在简洁方面(从数据和建模复杂性的角度来看), 特别是当时间序列中的规则模式被打乱时。我们表明, cir#不仅比考虑的基线模型表现更好, 而且比文献中报告的其他模型或方法具有更低的误差。

当大量搜索强度指标被作为旅游需求指标时, 传统的旅游需求预测模型将面临挑战。此外, 鉴于准确预测的重要性, 许多研究提出了新的混合模型或使用各种方法的组合。因此, 尽管采用更复杂的方法有明显的好处, 但风险在于处理难使用的模型。为了证明这种方法能有效地扩展到旅游业, 使用RMSE、MAE、MAPE或AvgReIMSE等标准指标来测试cir#的准确性。

cir#模型明显比文献中发现的其他模型简单, 并且不依赖于黑盒技术, 例如在神经网络或基于数据科学的模型中使用的技术。所进行的分析表明, cir#模型在误差的统计显著性方面优于其他参考预测。

这个模型作为Holt-Winters模型的一个拟议模型, 特别是在处理不规则数据时。

即使与文献中的其他模型相比, 所提出的模型也显示出优越性, 并且在数据模式中断时对旅游利益相关者做出决策特别有用。

创新之处在于所提出的模型是Holt-Winters模型的有效替代方案, 特别是当数据不规律时。此外, 与文献中的许多现有模型相比, cir#模型明显更简单、更透明, 避免了神经网络和基于数据科学的模型的“黑箱”性质。

Normalmente, la demanda turística tiende a seguir tendencias regulares, como temporadas altas y bajas a nivel trimestral/mensual y fines de semana y festivos a nivel diario. El conjunto de datos consiste en las pernoctaciones en Italia en establecimientos de alojamiento turístico recogidas mensualmente por Eurostat antes y durante la pandemia de COVID-19, rompiendo los patrones regulares.

El presente estudio pretende predecir las pernoctaciones en Italia en establecimientos de alojamiento turístico mediante un modelo estocástico no lineal de un solo factor denominado CIR#. La contribución de este estudio es doble: en términos de precisión de la predicción y en términos de parsimonia (tanto desde la perspectiva de los datos como de la complejidad de la modelización), especialmente cuando un patrón regular en la serie temporal se ve interrumpido. Demostramos que el CIR# no sólo aplica mejor que los modelos de referencia considerados, sino que también tiene un error mucho menor que otros modelos o enfoques adicionales de los que se informa en la literatura.

Los modelos tradicionales de previsión de la demanda turística pueden enfrentarse a desafíos cuando se adoptan cantidades masivas de índices de intensidad de búsqueda como indicadores de la demanda turística. Además, dada la importancia de unas previsiones precisas, muchos estudios han propuesto modelos híbridos novedosos o han utilizado diversas combinaciones de métodos. Así pues, aunque la adopción de enfoques más complejos presenta ventajas evidentes, el riesgo es el de enfrentarse a modelos poco manejables. Para demostrar cómo este enfoque puede extenderse de forma fructífera al turismo, se comprueba la precisión del CIR# utilizando métricas estándar como RMSE, MAE, MAPE o AvgReIMSE.

El modelo CIR# es notablemente más sencillo que otros modelos encontrados en la literatura y no se basa en técnicas de caja negra como las utilizadas en los modelos basados en redes neuronales o en la ciencia de datos. El análisis realizado sugiere que el modelo CIR# supera a otras predicciones de referencia en términos de significación estadística del error.

El modelo propuesto destaca por ser una opción viable al modelo Holt-Winters, sobre todo cuando se trata de datos irregulares.

El modelo propuesto ha demostrado su superioridad incluso cuando se compara con otros modelos de la bibliografía, y puede ser especialmente útil para los agentes del sector turístico a la hora de tomar decisiones cuando se producen alteraciones en los patrones de datos.

La novedad radica en que el modelo propuesto es una alternativa válida al Holt-Winters especialmente cuando los datos no son regulares. Además, en comparación con muchos modelos existentes en la literatura, el modelo CIR# es notablemente más sencillo y transparente, evitando la naturaleza de “caja negra” de los modelos basados en redes neuronales y en ciencia de datos.

Relational contracting is increasingly being applied to complex and uncertain construction projects. However, it has proved hard to achieve stable performance and industry-level learning in this field. This paper employs an institutional perspective to analyze how legitimacy for relational contracting has been produced and challenged in Denmark, Finland, Norway and Sweden, including implications for dissemination and learning.

A collaborative case study design is used, where longitudinal accounts of the developments in relational contracting over more than 25 years in four Nordic countries were developed by scholars based in each country. The descriptions are underpinned by literature sources from research, practice and policy.

The countries share similar problem perceptions that have triggered the de-institutionalization of traditional contracting practices. Models and policies developed elsewhere are important sources of knowledge and legitimacy. Most countries have seen pendulum movements, where dissemination of relational contracting is followed by backlashes when projects fail to meet projected outcomes. Before long, however, relational contracting tends to re-emerge under new labels and in slightly new forms. Such a proliferation of concepts presents further obstacles to learning. Successful institutionalization is found to rely on realistic goals in combination with broad competence development at the organizational and industry levels.

In seeking inspiration from other countries, policymakers should go beyond contract models to also consider strategies to manage industry-level learning.

The paper provides a unique longitudinal cross-country perspective on the field of relational contracting. As such, it contributes to the small stream of literature on long-term institutional change in the construction sector.

This paper explores the development of microcredentials (MCs) through a regional lens, considering their implications for lifelong learning and skilled labour mobility in Asia and the Pacific. It also examines recent global initiatives led by the United Nations Educational, Scientific and Cultural Organization (UNESCO).

The study reviews recent global initiatives aimed at developing shared definitions and principles for MCs, as led by UNESCO, taking into account the importance of existing initiatives such as the pilot project launched by the Australian government in 2022. The formalization of MCs within national education systems is also examined, with reference to frameworks developed by organizations like the European Union (EU), Australia and the Malaysia Qualifications Agency (MQA, 2020).

The paper underscores the increasing attention from international organizations and governments to the development and recognition of MCs, with significant efforts in countries like Australia and pilot initiatives in New Zealand and Malaysia. However, it also highlights that many MC projects in Asia are still in their early developmental stages, emphasizing the need for further progress and formalization.

This paper offers a unique perspective on the development of MCs in the Asia–Pacific region and their potential role in international education. It explores the prospects for MCs in international education, including student mobility and cross-border online courses. The challenge of integrating MCs into existing systems for recognizing international qualifications is discussed. Overall, the paper contributes to the understanding of MC frameworks as essential tools in the formalization of lifelong learning in the region, supported by the provided citations (OECD, 2021).

Examining the role of women on board (WoB) toward corporate sustainable growth (CSG) through leverage policy (LP). This research also investigates the interaction effect of WoB and LP on improving CSG.

This study uses a moderated mediation model to examine the impact of WoB on CSG, mediated by LP. Data from 48 KEHATI IDX ESG Sector Leaders Index companies observed from 2015 to 2021 were analyzed using the structural equation model partial least square (SEM-PLS) Warp.PLS 8.0. The research applies instrumental variables (IV) to test and control endogeneity due to nonrandom sample selection.

We found evidence that LP acts as a full mediator between the presence of WoB and CSG. The presence of WoB plays a moderate role by slightly weakening the influence of LP on CSG. Furthermore, we obtained evidence showing that the relationship between WoB and CSG is J-curve-shaped, a nonlinear relationship related to critical mass. Where the WoB ratio is at least 8.35% or higher, it will increase CSG in companies that have implemented the concept of environment social governance (ESG) in Indonesia.

This model uses a moderated mediation model and J-curve analysis; there is an interaction between WoB and LP on different paths of the mediator to CSG. This model examines the role of WoB as a moderator of the effect of LP on CSG. A nonlinear J-curve test was conducted to determine the minimum level of WoB that can influence the increase of CSG.