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Heating, ventilating, air-conditioning and cooling (HVAC) systems are crucial in daily health-care facility services. Design-related defects can lead to maintenance issues, causing service disruptions and cost overruns. These defects can be avoided if a link between the early design stages and maintenance feedback is established. This study aims to use experts’ experience in HVAC maintenance in health-care facilities to list and evaluate the risk of each maintenance issue caused by a design defect, supported by the literature.

Following semistructured interviews with experts, 41 maintenance issues were identified as the most encountered issues. Subsequently, a survey was conducted in which 44 participants evaluated the probability and impact of each design-caused issue.

Chillers were identified as the HVAC components most prone to design defects and cost impact. However, air distribution ducts and air handling units are the most critical HVAC components for maintaining healthy conditions inside health-care facilities.

The unavailability of comprehensive data on the cost impacts of all design-related defects from multiple health-care facilities limits the ability of HVAC designers to furnish case studies and quantitative approaches.

This study helps HVAC designers acquire prior knowledge of decisions that may have led to unnecessary and avoidable maintenance. These design-related maintenance issues may cause unfavorable health and cost consequences.

The impact of extreme heat on prisons and carceral facilities is becoming increasingly visible, yet remains overlooked by scholars, practitioners and policymakers. Prisons are a unique type of infrastructure designed to severely limit and control the movement of hundreds and even thousands of individuals as a form of punishment. This leads to many significant challenges to mitigating the risk of heat-related illness in prisons and other carceral spaces that have remained overlooked across many disciplines including emergency management, disasters, corrections and public health.

For this study, we analyzed 192 surveys from incarcerated persons in state prisons throughout Texas to understand how incarceration and the punitive prison environment create challenges to managing extreme heat in prisons.

We found that characteristics of modern incarceration, including communal distribution of resources, crowded conditions and a lack of agency for incarcerated people, create barriers to accessing resources during periods of extreme heat. Furthermore, the punitive nature of the prison environment as manifested in the relationship between staff and incarcerated persons and certain prison policies also create barriers to incarcerated persons accessing resources to reduce their risk of heat-related illness and death.

These issues are particularly relevant to the health and safety of incarcerated persons during periods of extreme temperatures but also speak broadly to the implications of incarceration, disaster risk, and the advancement of human rights for incarcerated people.

This article addresses a gap in the literature by including the perspectives of persons incarcerated in Texas prisons experiencing extreme heat and implicates the characteristics of incarceration and punishment in the production of disaster risk.

Awarding contracts based solely on the lowest price is unsuitable for every project. Consequently, most procurement systems in developed countries have progressed to the multicriteria selection practices (MSPs) for tender evaluation. MSPs consider a range of quality measures, such as completion time, life cycle cost, functional characteristics, environmental impact and innovation, alongside bid price. This study examines the prevailing MSPs in Sri Lankan public tender evaluations to enhance the effectiveness of the local tender evaluation process.

A desk study approach was employed to collect bidding documents, resulting in the identification of 66 documents. A systematic screening process was then applied to identify those bidding documents that incorporated MSPs. Subsequently, content analysis was conducted to determine the common features of the functions used in MSPs.

The study identified six primary functions related to MSPs incorporated in the bidding documents to procure building and substation projects. Three functions follow the price-to-quality method, while the remaining three follow the quality-to-price method. Among these identified functions, four functions employ objective evaluation criteria, such as thickness, capacity and operational loss. The other two functions utilize subjective evaluation criteria, such as the project’s design and technical specifications. Contract awarding will be based on either the highest score or the lowest bid, depending on the function type.

This study’s originality lies in exploring MSPs in the Sri Lankan public tender evaluation process and in disclosing their characteristics to promote the MSPs in Sri Lanka and developing countries.

There is a knowledge gap regarding the determinants of open innovation processes and outcomes in a joint value creation context, as well as what role artificial intelligence (AI) and data management play in facilitating open innovation processes. One strategy to better understand joint value creation through open innovation, supported by AI and data management, is to conduct studies on the digital business ecosystem (DBE). The purpose of this paper is to improve our current knowledge of this urgent issue in contemporary management through the lens of an ecosystem-based theory by conducting an empirical study on two DBEs (called ecosystem micro-communities (EMCs)), developed by Haier, as well as multiple literature reviews on the key concepts “Haier EMC” and “digital business ecosystem”.

By building on multiple literature reviews and empirical data from a multi-year and ongoing research program driven by Haier, this study examines Haier’s EMC model for AI-driven DBEs. Secondary data were collected through iterative literature reviews on DBEs, the EMC concept and the two selected EMC cases. The empirical data were collected through a qualitative study of two Haier EMCs in China.

Haier's ecosystem micro-community concept represents a radical shift towards a more flexible, responsive and innovative cross-industry organizational structure, offering valuable lessons for business leaders and scholars. Haier’s ecosystem micro-community model, part of their RenDanHeYi philosophy and here viewed as a DBE, is a pioneering management concept that not only redefines the management of the firm and the traditional corporate structure, but also the traditional view on innovation management, business strategy, human resource management and marketing (customer centricity). The concept has therefore an important and big impact on traditional management. For scholars, the gap in understanding innovation processes in open business ecosystems is addressed by the concept. However, the concept also opens new areas for academic research, particularly in innovation management, business strategy, human resource management and marketing. The concepts further encourage more interdisciplinary research.

The DBE is a relatively new research area that will need more research. While the EMC model is promising as an effective version of a DBE, its effectiveness across different industries and organizational cultures needs to be explored further. Future research should investigate its applicability and impact in diverse business environments. To understand the EMC’s long-term impact, longitudinal studies are needed. These should focus on the sustained competitive advantages, potential market disruptions and the evolution of customer value propositions over time. Finally, considering increasing concerns about data privacy and security, future research should also explore how DBEs solve the issue of data protection and IP while promoting open innovation and value sharing.

For managers and practitioners, the EMC concept could inspire leaders to learn how to foster innovation by creating smaller, autonomous teams that can respond quickly to market changes in the form of a DBE. The concepts exemplify how value creation and capture could be enhanced for any company and even could be a new strategy in the company’s digital transformation and repositioning into a more competitive, high-end player on the market. The concept also emphasizes employee empowerment and ownership, which can lead to higher job satisfaction and retention rates. The concept can further improve companies’ adaptability and resilience by decentralizing decision-making. Finally, the micro-communities allow businesses to be more customer-centric, developing products and services that better meet specific customer needs.

The social implications could be positive, as complex social problems commonly need an ecosystem approach to develop and deliver impactful solutions. In addition, Haier’s ecosystem micro-community model seems inherently scalable and culturally adaptable.

Haier’s EMC model is well-known in the research literature and is a novel approach to DBEs, which has been proven successful and replicable in different countries and industries. Providing insights from multiple literature reviews and two unique Haier EMC cases will contribute to a better understanding of highly effective data- and AI-driven business ecosystems, as well as of determinants of open innovation processes and outcomes in a joint value creation context, as well as what role AI and data management play in facilitating open innovation processes.

This paper aims to answer questions pertinent to whether or not services provided by smart hotels are really what customers are looking for, as well as to ascertain what are some unintended experiences guests may encounter. In essence, to the best of the authors’ knowledge, this research is the first in the field to acknowledge the paradox of smart service.

This inquiry adopts a qualitative approach with data-driven from online customer reviews and semistructured interviews. Thematic analysis was undertaken to interpret review comments.

Results point to a new phenomenon, which is coined as the smartness paradox. In particular, customers on one hand enjoy an array of smart-infused experiences that jointly offer patrons a sense of a futuristic lifestyle. On the other hand, smart devices superimpose a number of hindrances that bring guests dismay and annoyance.

This investigation brings smart service failure to the fore to highlight several key failure themes that could jeopardize the entire operation with debased customers’ satisfaction and loyalty inclination.

The smartness-paradox framework used in the present inquiry entails both approach and avoidance consequences customers enact depending on their smart experiences.

This study aims to find the key drivers of green innovation in family firms by examining firm characteristics and geographical factors. It seeks to develop a conceptual framework that explains how internal resources and external environments influence environmental innovation practices in these businesses.

Using machine learning (ML) methods, this study develops a predictive model for green innovation in family firms, drawing on data from 3,289 family businesses across 27 EU Member States and 12 additional countries. The study integrates the Resource-Based View (RBV) and Location Theory to analyze the impact of firm-level resources and geographical contexts on green innovation outcomes.

The results show that both firm-specific resources, such as size, digital capabilities, years of operation and geographical factors, like country location, significantly influence the likelihood of family firms engaging in environmental innovation. Larger, technologically advanced firms are more likely to adopt sustainable practices, and geographic location is crucial due to different regulatory environments and market conditions.

The findings reinforce the RBV by showing the importance of firm-specific resources in driving green innovation and extend Location Theory by emphasizing the role of geographic factors. The study enriches the theoretical understanding of family businesses by showing how noneconomic goals, such as socioemotional wealth and legacy preservation, influence environmental innovation strategies.

Family firms can leverage these findings to enhance their green innovation efforts by investing in technology, fostering sustainability and recognizing the impact of geographic factors. Aligning innovation strategies with both economic and noneconomic goals can help family businesses improve market positioning, comply with regulations and maintain a strong family legacy.

This research contributes a new perspective by integrating the RBV and Location Theory to explore green innovation in family firms, highlighting the interplay between internal resources and external environments. It also shows the effectiveness of machine learning methods in predicting environmental innovation, providing deeper insights than traditional statistical techniques.

This study aims to conduct environmental comfort analyses of vernacular architecture to establish design principles for a more sustainable design domain. In the scope of this research, 47 individual Harran earthen houses, comprising 32 different types and six typologies are examined.

Environmental comfort is selected as an umbrella term for the analysis of thermal comfort, visual comfort, and natural ventilation performance criteria. The performance simulations are conducted utilizing ClimateStudio and SolidWorks software. These simulations yield values for thermal comfort, glare, daylight, solar radiation, airflow, and pressure, which are used to compare different Harran earthen house typologies.

The study’s results indicate that various environmental comfort standards are met by Harran earthen houses through passive systems, without the need for technology or mechanical equipment. In terms of thermal comfort, visual comfort, and natural ventilation performance criteria, a typology that has advantages in one criterion may have disadvantages in the others. Factors such as orientation, material selection, opening arrangement, and architectural form are found to have an impact on environmental comfort.

This study differs from previous Harran earthen house and environmental comfort studies by focusing on multiple performance criteria and conducting a typology-based comparison based on performance analysis. The results of the study are expected to provide valuable insights into the environmental comfort studies of Harran earthen houses, emphasizing their relevance and applicability in contemporary architectural and urban design.

This paper presents a new and well-structured framework that aims to assess the current environmental impact from a Greenhouse Gas (GHG) emissions perspective. This tool includes a new set of Lean Key Performance Indicators (KPIs), which translates the well-known logic of Overall Equipment Effectiveness in the field of GHG emissions, that can progressively detect industrial losses that cause GHG emissions and support decision-making for implementing improvements.

The new metrics are presented with reference to two different perspectives: (1) to highlight the deviation of the current value of emissions from the target; (2) to adopt a diagnostic orientation not only to provide an assessment of current performance but also to search for the main causes of inefficiencies and to direct improvement implementations.

The proposed framework was applied to a major company operating in the plywood production sector. It identified emission-related losses at each stage of the production process, providing an overall performance evaluation of 53.1%. The industrial application shows how the indicators work in practice, and the framework as a whole, to assess GHG emissions related to industrial losses and to proper address improvement actions.

This paper scrutinizes a new set of Lean KPIs to assess the industrial losses causing GHG emissions and identifies some significant drawbacks. Then it proposes a new structure of losses and KPIs that not only quantify efficiency but also allow to identify viable countermeasures.

Nearly 75% of EU buildings are not energy-efficient enough to meet the international climate goals, which triggers the need to develop sustainable construction techniques with high degree of resilience against climate change. In this context, a promising construction technique is represented by ventilated façades (VFs). This paper aims to propose three different VFs and the authors define a novel machine learning-based approach to evaluate and predict their energy performance under different boundary conditions, without the need for expensive on-site experimentations

The approach is based on the use of machine learning algorithms for the evaluation of different VF configurations and allows for the prediction of the temperatures in the cavities and of the heat fluxes. The authors trained different regression algorithms and obtained low prediction errors, in particular for temperatures. The authors used such models to simulate the thermo-physical behavior of the VFs and determined the most energy-efficient design variant.

The authors found that regression trees allow for an accurate simulation of the thermal behavior of VFs. The authors also studied feature weights to determine the most relevant thermo-physical parameters. Finally, the authors determined the best design variant and the optimal air velocity in the cavity.

This study is unique in four main aspects: the thermo-dynamic analysis is performed under different thermal masses, positions of the cavity and geometries; the VFs are mated with a controlled ventilation system, used to parameterize the thermodynamic behavior under stepwise variations of the air inflow; temperatures and heat fluxes are predicted through machine learning models; the best configuration is determined through simulations, with no onerous in situ experimentations needed.

Green walls are vertical structures with various plant species that contribute to achieving sustainability in terms of environmental, economic and social aspects. A comparison of green wall performance with a similar type of conventional wall would be the most convincing way of promoting green wall applications than comparing the performance within types of green walls. Hence, this study evaluated the life cycle cost (LCC) of an indirect green facade with a conventional wall in the Sri Lankan tropical climate towards enhancing the adaptation of the green wall concept as an energy-saving solution.

The study involved two stages: (1) assessing the thermal performance and (2) calculating the LCC of the indirect green facade and the conventional wall. On-site temperature measurements were taken from various spots on the exterior and interior wall surfaces of each building in different time intervals per day for 21 days from the end of May to the beginning of July. The LCC analysis was performed using the cost data collected through the market survey and document review.

The temperature difference between the external and internal wall surfaces of the conventional wall (1.06⁰C) is higher than the green wall (0.32⁰C). This implies that green walls help retain 2/3 of the temperature transferred through a conventional wall, thereby reducing the energy requirement for cooling purposes by 70%. Though the initial cost of a green wall is 19% higher than a conventional wall, maintenance costs of green walls result in 29% savings. This results in a 55% overall annual LCC savings compared to conventional walls.

There is a dearth of studies to evidence that the cost performance of green walls is more effective than conventional walls in tropical climates, and this study fulfils this research gap. Thus, the findings would be more convincing to clients towards enhancing green wall applications.