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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.

The objective of this paper is to examine the determinants of the Greek house market during the period 2006–2022 using not only economic variables but also behavioral variables, taking advantage of available information on the volume of Google searches. In order to quantify the behavioral variables, we implement a Python code using the Pytrends 4.9.2 library.

In our study, we assert that models relying solely on economic variables, such as GDP growth, mortgage interest rates and inflation, may lack precision compared to those that integrate behavioral indicators. Recognizing the importance of behavioral insights, we incorporate Google Trends data as a key behavioral indicator, aiming to enhance our understanding of market dynamics by capturing online interest in Greek real estate through searches related to house prices, sales and related topics. To quantify our behavioral indicators, we utilize a Python code leveraging Pytrends, enabling us to extract relevant queries for global and local searches. We employ the EGARCH(1,1) model on the Greek house price index, testing several macroeconomic variables alongside our Google Trends indexes to explain housing returns.

Our findings show that in some cases the relationship between economic variables, such as inflation and mortgage rates, and house prices is not always consistent with the theory because we should highlight the special conditions of the examined country. The country of our sample, Greece, presents the special case of a country with severe sovereign debt issues, which at the same time has the privilege to have a strong currency and the support and the obligations of being an EU/EMU member.

The results suggest that Google Trends can be a valuable tool for academics and practitioners in order to understand what drives house prices. However, further research should be carried out on this topic, for example, causality relationships, to gain deeper insight into the possibilities and limitations of using such tools in analyzing housing market trends.

This is the first paper, to the best of our knowledge, that examines the benefits of Google Trends in studying the Greek house market.

In academia, assessment is often narrow in its focus on research productivity, its application of a limited number of standardised metrics and its summative approach aimed at selection. This approach, corresponding to an exclusive, subject-oriented concept of talent management, can be thought of as at odds with a broader view of the role of academic institutions as accelerating and improving science and scholarship and its societal impact. In recent years, open science practices as well as research integrity issues have increased awareness of the need for a more inclusive approach to assessment and talent management in academia, broadening assessment to reward the full spectrum of academic activities and, within that spectrum, deepening assessment by critically reflecting on the processes and indicators involved (both qualitative and quantitative). In terms of talent management, this would mean a move from research-focused assessment to assessment including all academic activities (including education, professional performance and leadership), a shift from focus on the individual to a focus on collaboration in teams (recognising contributions of both academic and support staff), increased attention for formative assessment and greater agency for those being evaluated, as well as around the data, tools and platforms used in assessment. Together, this represents a more inclusive, subject-oriented approach to talent management. Implementation of such changes requires involvement from university management, human resource management and academic and support staff at all career levels, and universities would benefit from participation in mutual learning initiatives currently taking shape in various regions of the world.

The purpose of this paper is to assess the appropriateness of equal weights estimation (sumscores) and the application of the composite equivalence index (CEI) vis-à-vis differentiated indicator weights produced by partial least squares structural equation modeling (PLS-SEM).

The authors rely on prior literature as well as empirical illustrations and a simulation study to assess the efficacy of equal weights estimation and the CEI.

The results show that the CEI lacks discriminatory power, and its use can lead to major differences in structural model estimates, conceals measurement model issues and almost always leads to inferior out-of-sample predictive accuracy compared to differentiated weights produced by PLS-SEM.

In light of its manifold conceptual and empirical limitations, the authors advise against the use of the CEI. Its adoption and the routine use of equal weights estimation could adversely affect the validity of measurement and structural model results and understate structural model predictive accuracy. Although this study shows that the CEI is an unsuitable metric to decide between equal weights and differentiated weights, it does not propose another means for such a comparison.

The results suggest that researchers and practitioners should prefer differentiated indicator weights such as those produced by PLS-SEM over equal weights.

To the best of the authors’ knowledge, this study is the first to provide a comprehensive assessment of the CEI’s usefulness. The results provide guidance for researchers considering using equal indicator weights instead of PLS-SEM-based weighted indicators.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.

The study explores the possibility of developing a valid scale for integrated management of heritage sites to bring a holistic approach to heritage properties' conservation practices.

The study followed the exploratory sequential method. An in-depth interview was adopted for exploring indicators, and a questionnaire survey was administered for descriptive analysis.

Cultural resources conservation strategies have been analyzed from a tourist, local communities and stakeholder's perspective with local-specific indicators. The study indicates that a multi-dimensional approach that integrates tourists, local communities and other stakeholders-based indicators can be developed at the destination level for the integrated management of heritage properties. Tourist-centric, local community-specific and stakeholder-oriented approaches could act as catalysts for more pragmatic conservation practices in the local areas based on the site-specific indicators.

The study is limited to lesser-known heritage sites located in the southern provincial states of India. The technical conservation strategies on the structure and architecture are not part of the study. Theoretical implications on the study of this kind can contribute to the literature as it throws light on future studies seeking local-centric conservation and management practices of heritage sites hitherto less explored in the domain of conservation science. The scale provides insight into the appropriate form of intervention that the local communities, tourists and other stakeholders can do at the heritage sites, hence the possibility of garnering the attention of other discipline strivings towards the conservation of heritage sites and to apply along with other relevant variables. It is expected that the study might expedite the knowledge accumulation in conservation science.

The scale can be used in a similar context for the integrated management of heritage sites. The study can assist the policymakers and planners in seeking the support of stakeholders, local communities and tourists for the implementation of heritage conservation and management programs. Such a local-centric management strategy promoting responsible consumption and production could contribute to SDG 12. Further, the study can also contribute towards SDG 11.4, which calls for strengthening the effort to protect and safeguard cultural and natural heritage. This scale can be a tool for destination management organizations (DMOs) to understand the level of intervention of local communities, tourists and other stakeholders at the heritage site.

The integrated management approach of heritage conservation immensely helps the lesser-known heritage sites the world over as such structures are out of the focal point of government funding and other conservation efforts. The synergy of the integrated approach could protect lesser-known unfunded heritage sites, and thereby, the cultural reflections of the community concerned can be made available for future visitors’ consumption.

The study attempted to understand the conservation approaches for lesser-known heritage sites with the support of both demand and supply-side stakeholders. Such a collaborative approach is the first of this kind in the conservation of heritage sites in India.

Lean management tools are becoming increasingly applied in different types of organizations around the world. These tools have shown their significant contribution to improving business performance. In this vein, the purpose of this paper is to examine the influence of lean management on both occupational safety and operational excellence in Tunisian companies.

A survey was conducted among Tunisian companies, and it resulted in the collection of 62 responses that were analyzed using the software SPSS. In addition, a conceptual model linking the practices of the three basic concepts was designed to highlight the hypotheses of the research. Subsequently, factor analysis and structural equation method analysis were conducted to assess the validation of the assumptions.

The results obtained have shown that lean management has a significant impact on occupational safety. Similarly, occupational safety has a significant impact on operational excellence. However, lean management does not have a significant impact on operational excellence.

This work highlighted the involvement of small and medium-sized enterprise’s managers from emerging economies in the studied concepts’ practices. Likewise, it testified to the impacts of lean management on occupational safety and operational excellence in the Tunisian context.

The purpose of the study is to investigate the factors that influence the adoption of new sustainability reporting (SDG) and external assurance (EXTA) practices. This study also examines the relationship between sustainability reporting activity and corporate economic performance for a sample of 99 companies in Gulf Cooperation Council (GCC) countries that addressed SDGs in their sustainability reports published in 2019.

Using a two-stage analysis, this study examines how firms’ characteristics and corporate governance variables affect SDG and economic performance, as well as the firm’s decision to adopt EXTA statements for a sample of companies in that addressed SDGs in their sustainability reports published in 2019. The authors collected data from the Global Reporting Initiative’s (GRI) Sustainability Disclosure database and the Bureau van Dijk for Orbis database.

The results show that the variables firm size, profitability, big 4 auditors and government ownership significantly affect SDG and economic performance. The results also reveal that firms operating in the manufacturing sector are positively correlated with SDG and the firm’s decision to adopt EXTA statements. Furthermore, the results indicate that board independence positively affects SDGs and EXTA.

The results can be particularly relevant and timely in helping large GCC companies promote their engagement to sustainable development practices by adopting more sustainable long-term strategies and policies. The findings could also guide managers in the strategic direction to identify firms’ characteristics and corporate governance features essential to promote sustainability reporting, an increasingly important performance indicator for investors and to enhance their confidence in the capital market. The results may also have practical implications to policymakers and other regulators in GCC countries to define effective frameworks that promote sustainable development reports and the use of EXTA.

The results make significant contributions by providing new insights to the existing literature on sustainability reporting in emerging markets by examining a unique perspective on the influence of firms’ characteristics and corporate governance features on the adoption of new sustainability reporting practices. The authors further add to the previous literature on the relationship between a firm’s economic performance and sustainable reporting by providing evidence from large companies in GCC countries, which might benefit from the adoption of multiple conceptual lenses, in this case, legitimacy and stakeholder theories. Lastly, through the empirical findings, this study provides economic validity to the 2018 joint initiative of the GRI and the United Nations Global Compact to strengthen corporate actions to achieve the United Nations SDGs.

In the context of investment decisions, the intricate interplay between exogenous shocks and their influence on investor confidence significantly shapes their behaviors and, consequently, their outcomes. Investment decisions are influenced by uncertainties, exogenous shocks as well as the sentiments and confidence of investors, factors typically overlooked by decision-makers. This study will meticulously examine these multifaceted influences and discern their intricate hierarchical nuances in the sentiments of industrial entrepreneurs during the COVID-19 pandemic.

Employing the robust framework of the generalized linear latent and mixed models (GLLAMM), this research will thoroughly investigate individual and group idiosyncrasies present in diverse data compilations. Additionally, it will delve deeply into the exogeneity of disturbances across different sectors and regions.

Relevant insights gleaned from this research elucidate the adverse influence of exogenous forces, including pandemics and financial crises, on the confidence of industrial entrepreneurs. Furthermore, a significant discovery emerges in the regional analysis, revealing a notable homogeneity in the propagation patterns of industrial entrepreneurs' perceptions within the sectoral and regional context. This finding suggests a mitigation of regional effects in situations of global exogenous shocks.

Within the realm of academic inquiry, this study offers an innovative perspective in unveiling the intricate interaction between external shocks and their significant impacts on the sentiment of industrial entrepreneurs. Furthermore, the utilization of the robust GLLAMM captures the hierarchical dimension of this relationship, enhancing the precision of analyses. This approach provides a significant impetus for data-informed strategic directions.

The purpose of this paper is to investigate how different manufacturing technologies are bundled together and how these bundles influence operations performance and, indirectly, business performance. With the emergence of Industry 4.0 (I4.0) technologies, manufacturing companies can use a wide variety of advanced manufacturing technologies (AMT) to build an efficient and effective production system. Nevertheless, the literature offers little guidance on how these technologies, including novel I4.0 technologies, should be combined in practice and how these combinations might have a different impact on performance.

Using a survey study of 165 manufacturing plants from 11 different countries, we use factor analysis to empirically derive three distinct manufacturing technology bundles and structural equation modeling to quantify their relationship with operations and business performance.

Our findings support an evolutionary rather than a revolutionary perspective. I4.0 technologies build on traditional manufacturing technologies and do not constitute a separate direction that would point towards a fundamental digital transformation of companies within our sample. Performance effects are rather weak: out of the three technology bundles identified, only “automation and robotization” have a positive influence on cost efficiency, while “base technologies” and “data-enabled technologies” do not offer a competitive advantage, neither in terms of cost nor in terms of differentiation. Furthermore, while the business performance impact is positive, it is quite weak, suggesting that financial returns on technology investments might require longer time periods.

Relying on a complementarity approach, our research offers a novel perspective on technology implementation in the I4.0 era by investigating novel and traditional manufacturing technologies together.