Justin B. Keeler, Victoria McKee, Meagan E. Brock-Baskin
et al.
This study investigates how an organizational bottom-line mentality (BLM) climate influences employee perceptions of workplace ostracism, with a focus on the moderating role of zero-sum beliefs (ZSB). Using a survey of 220 full-time respondents in the United States, we conducted regression analysis and moderated mediation tests via Hayes' PROCESS macro in R. We measured perceived organizational BLM, employee BLM, ZSB, and work ostracism using validated scales. The results supported all five hypotheses. An organizational BLM climate positively influences employee BLM, which in turn increases perceptions of ostracism. This relationship is stronger for employees with high ZSB. Additionally, the indirect effect of an organizational BLM climate on ostracism through employee BLM is significantly moderated by ZSB. The findings highlight that employees with high ZSB experience greater ostracism in high-BLM environments. Drawing on Social Cognitive Theory (SCT) this study investigates and underscores the negative interpersonal outcomes of a BLM climate. By addressing zero-sum beliefs through targeted interventions, organizations can balance financial objectives with employee well-being, enhancing workplace dynamics and morale.
Flooding disasters have extensively disrupted productive activities, causing market uncertainties. However, how these uncertainties affect institutional investors' strategies in the commercial property market remains an underexplored question. To address the question, we provide a novel perspective by classifying flooding events into seasonal and climate change-induced (CCI) floods. Specifically, we conduct a spatial quasi-natural experiment to examine the treatment effect of seasonal and CCI floods on the commercial property market in Chinese cities from 2010 to 2018. We find that flooding disasters create a discount effect on property prices, which lures investors to flock into the market. However, institutional investors perform more cautiously in properties within CCI floodplains relative to counterparts within seasonal floodplains. In addition, local institutional investors benefit from higher discount premiums more than non-local institutional investors in floodplain markets, though this advantage diminishes in CCI floodplain markets. Our findings provide valuable implications for investors’ decision-making in flood-prone cities. Policymakers are encouraged to promote market information transparency and resilience-building initiatives to mitigate the adverse effects of flooding events on local economies.
Tammy Mackenzie, Branislav Radeljic, Leslie Salgado
et al.
This systematic review examines peer-reviewed studies on application of GPT in business management, revealing significant knowledge gaps. Despite identifying interesting research directions such as best practices, benchmarking, performance comparisons, social impacts, our analysis yields only 42 relevant studies for the 22 months since its release. There are so few studies looking at a particular sector or subfield that management researchers, business consultants, policymakers, and journalists do not yet have enough information to make well-founded statements on how GPT is being used in businesses. The primary contribution of this paper is a call to action for further research. We provide a description of current research and identify knowledge gaps on the use of GPT in business. We cover the management subfields of finance, marketing, human resources, strategy, operations, production, and analytics, excluding retail and sales. We discuss gaps in knowledge of GPT potential consequences on employment, productivity, environmental costs, oppression, and small businesses. We propose how management consultants and the media can help fill those gaps. We call for practical work on business control systems as they relate to existing and foreseeable AI-related business challenges. This work may be of interest to managers, to management researchers, and to people working on AI in society.
This study examines whether the tariff policies delivered on promises to revitalize American manufacturing and create jobs. Using county-level business application data from 2018-2025, we analyze the relationship between tariff implementation and new business formation through linear regression analysis. Our findings reveal a statistically significant positive association between US tariffs on China and American business applications. However, when Chinese retaliatory tariffs are included in the analysis, their negative coefficient substantially exceeds the positive US tariff effect, suggesting that retaliatory measures largely offset the benefits of protectionist policies. Control variables including inflation rate, federal funds rate, and government spending show significant positive effects on business formation. These results indicate that while protectionist trade policies may stimulate domestic business formation, their effectiveness is significantly diminished by retaliatory responses from trading partners. The study provides evidence that unilateral tariff measures without diplomatic coordination produce limited net benefits, confirming that trade wars create scenarios where potential gains are neutralized by counteractions.
Robert Welch, Charles Laughton, Oliver Henrich
et al.
A range of computational biology software (GROMACS, AMBER, NAMD, LAMMPS, OpenMM, Psi4 and RELION) was benchmarked on a representative selection of HPC hardware, including AMD EPYC 7742 CPU nodes, NVIDIA V100 and AMD MI250X GPU nodes, and an NVIDIA GH200 testbed. The raw performance, power efficiency and data storage requirements of the software was evaluated for each HPC facility, along with qualitative factors such as the user experience and software environment. It was found that the diversity of methods used within computational biology means that there is no single HPC hardware that can optimally run every type of HPC job, and that diverse hardware is the only way to properly support all methods. New hardware, such as AMD GPUs and Nvidia AI chips, are mostly compatible with existing methods, but are also more labour-intensive to support. GPUs offer the most efficient way to run most computational biology tasks, though some tasks still require CPUs. A fast HPC node running molecular dynamics can produce around 10GB of data per day, however, most facilities and research institutions lack short-term and long-term means to store this data. Finally, as the HPC landscape has become more complex, deploying software and keeping HPC systems online has become more difficult. This situation could be improved through hiring/training in DevOps practices, expanding the consortium model to provide greater support to HPC system administrators, and implementing build frameworks/containerisation/virtualisation tools to allow users to configure their own software environment, rather than relying on centralised software installations.
We give an alternative construction of Totaro's weight filtration on singular homology of the real points of a real algebraic variety. Our construction shows that this filtration comes from Bondarko's weight filtration on Voevodsky motives.
Murunga Anthony Ekisa Amoo, Charles Mallans Rambo, John Mwaura Mbugua
Objective: This study examined the extent to which liquidity risk management practices influence the performance of real estate construction housing projects in Busia County, Kenya. It aimed to assess how effective liquidity management contributes to project continuity, financial stability, and successful completion. Theoretical Framework: The study was anchored on financial management theory and risk control theory. These frameworks provide a basis for understanding how liquidity risk management can mitigate project delays, cost overruns, and financial instability in the construction sector. Method: A descriptive survey design was adopted. The target population included 1,832 stakeholders in real estate construction, from which a sample of 298 was selected through stratified and simple random sampling techniques. Data collection tools included structured questionnaires, interview schedules, checklists, and focus group discussions. Quantitative data were analyzed using descriptive statistics, Pearson correlation, and simple linear regression in SPSS version 25. Qualitative data were analyzed thematically. Results and Discussion: The study tested the null hypothesis that there is no significant relationship between liquidity risk management practices and project performance at a 0.025 level of significance. The hypothesis was rejected (p = 0.000), indicating a statistically significant positive relationship. Effective liquidity planning enhanced financial flow, minimized delays, and improved project outcomes, confirming theoretical expectations. Research Implications: The study offers insights for developers, managers, and policymakers on the need to institutionalize strong liquidity risk frameworks to ensure project sustainability. Originality/Value: This study contributes original empirical evidence from a developing economy, addressing gaps in construction finance literature and supporting strategic decision-making in real estate development.
Hybrid ground source heat pump systems (GSHP) offer energy flexibility in operation. For hybrid GSHP systems coupled with district heating, limited studies investigated control strategies for reducing system energy costs from the perspective of building owners. This study proposed a cost-effective control strategy for a hybrid GSHP system integrated with district heating, investigating how power limits of district heating/GSHP, COP value for control (COPctrl), and control time horizon impact the system annual energy cost, CO2 emissions, and long-term borehole heat exchanger system performance. The simulations were performed using the dynamic building simulation tool IDA ICE 4.8. The results indicate that to realize both the energy cost savings and the long-term operation safety, it is essential to limit the heating power of district heating/GSHP and select an appropriate COPctrl. The control time horizon insignificantly affected the annual energy cost and long-term borehole heat exchanger system performance. The recommended COPctrl was 3.6, which is near the GSHP seasonal performance factor. Eventually, the cost-effective control reduced the system’s annual energy cost by 2.2% compared to the GSHP-prioritized control. However, the proposed control increased the CO2 emissions of the hybrid GSHP system due to the higher CO2 emissions from district heating.
Despite its potential to increase sustainability, productivity, and efficiency, the construction industrys adoption of the Smart City and Industry 4.0 (SC&I4) concept has been considered sluggish. Previous studies have clarifiedthat more study and development in this technology area is needed to increase its implementation in the completion of construction projects. The objective of this paperis to conduct a review of SC&I4 constructionrelated domains to identify the area of focus of previous studies.This study used a bibliometric approach, and the data were extracted from the Scopus database. The database was searched using keywords like “smartcity,” “Industry 4.0,” and “construction” to retrieve relevant documents. Based on the collected bibliographic data, a network and overlay visualisation map of the cooccurrence keywords was created using VOSviewer. The results showed that past studies prioritised SC&I4 project delivery, blockchain and sustainable development, and the Internet of Things. Additionally, the present focus of this fields research is moving toward a more digitalised application of SC&I4, particularly in blockchain technology. The results highlight a knowledge vacuum that developing nations, particularly those in South America, Asia, and Africa, might investigate to enhance the delivery of construction projects across the continent via SC&I4. This paper contributes to SC&I4discourse, which has not received much attention in recent bibliometric and scientometric studies.
Real estate business, Regional economics. Space in economics
Asmae El Jaouhari, Ashutosh Samadhiya, Anil Kumar
et al.
In the rapidly evolving real estate industry, integrating automated valuation models (AVMs) has become critical for improving property assessment accuracy and transparency. Although there is some research on the subject, no thorough qualitative systematic review has been done in this field. This paper aims to provide an up-to-date and systematic understanding of the strategic applications of AVMs across various real estate subsectors (i.e., real estate development, real estate investment, land administration, and taxation), shedding light on their broad contributions to value enhancement, decision-making, and market insights. The systematic review is based on 97 papers selected out of 652 search results with an application of the PRISMA-based method. The findings highlight the transformative role of AVMs approaches in streamlining valuation processes, enhancing market efficiency, and supporting data-driven decision-making in the real estate industry, along with developing an original conceptual framework. Key areas of future research, including data integration, ethical implications, and the development of hybrid AVMs approaches are identified to advance the field and address emerging challenges. Ultimately, stakeholders can create new avenues for real estate valuation efficiency, accuracy, and transparency by judiciously utilizing AVMs approaches, leading to more educated real estate investment decisions.
Состояние конкурентной среды в сфере коммунального хозяйства является одним из ключевых факторов, определяющих эффективность функционирования жилищно-коммунальной инфраструктуры. Повышение конкуренции в этой сфере способно оказать существенное влияние на качество предоставляемых услуг, их доступность для населения, а также на модернизацию и развитие коммунальных систем.
В настоящее время отрасль коммунального хозяйства характеризуется высокой степенью монополизации, недостаточным уровнем частных инвестиций и низкой инновационной активностью. Это обусловлено рядом факторов, в том числе сложностью входа на рынок для новых игроков, отсутствием эффективных механизмов государственно-частного партнерства, непрозрачностью тарифообразования и неразвитостью системы контроля качества услуг. Кроме того, высокая изношенность коммунальной инфраструктуры и недостаточное финансирование ее модернизации создают дополнительные барьеры для развития конкуренции.
Для решения этих проблем необходимо реализовать комплекс мер, направленных на развитие конкуренции в сфере коммунального хозяйства. Это может включать в себя стимулирование передачи объектов коммунальной инфраструктуры в концессию, аренду, а также создание благоприятных условий для участия субъектов малого и среднего предпринимательства в оказании коммунальных услуг. Кроме того, важно обеспечить прозрачность функционирования отрасли, совершенствование тарифного регулирования и внедрение современных цифровых технологий.
Внедрение таких мер позволит повысить эффективность работы коммунального комплекса, улучшить качество и доступность коммунальных услуг для населения, а также создать предпосылки для привлечения частных инвестиций в модернизацию инфраструктуры. Это, в свою очередь, будет способствовать развитию конкуренции и повышению конкурентоспособности отрасли в целом. Важную роль в этом процессе должны сыграть органы государственной власти, реализуя комплексную политику поддержки и стимулирования конкуренции в сфере коммунального хозяйства.
In this paper, we have constructed a VAR model to identify and assess the impact of real interest rate shocks, real estate demand, oil prices, uncertainty, and aggregate business activity on residential real estate prices in Russia. The relevance of the research is due to the following: the dynamics of real estate prices determines the consumer and investment behavior of households, and serious fluctuations in real estate prices lead to adverse consequences in many areas of life, so more and more researchers are asking questions about the presence of bubbles in the real estate market, which can be dangerous to the stability of the economy. In addition, a sharp increase in the cost of housing in Russia in 2020 is an open question for researchers. Our goal is to determine what factors caused the rise in real estate prices in Russia in the time interval from the Q1 of 2000 to the Q2 of 2022. A VAR model with a Cholesky decomposition was used for the evaluation. Several specifications were considered with the inclusion of the real oil price as an exogenous variable and a set of endogenous variables: real GDP, real interest rate, uncertainty index and housing price index. The main conclusion of the paper is that the housing market is sensitive to identified macroeconomic shocks, and a decrease in the interest rate leads to an increase in demand and real estate prices. The estimate of the long-term elasticity of housing prices for oil prices was 0.35, the dynamics of oil prices explained a significant proportion of the variation in real estate prices, but the predominant role in housing price fluctuations is given to housing demand shocks. The housing demand shocks in Russia itself had a negligible impact on GDP.
The purpose of this study is to identify the knowledge of the real estate market by students and graduates and to find out their housing and investment plans. A literature study relating to the real estate market was conducted. The diagnostic survey method was used to determine the housing and investment plans of students and graduates, their expectations regarding changes in housing policy, and their level of knowledge of basic concepts and relationships related to the real estate market. The survey was conducted in March 2022 among students and graduates of Wroclaw University of Economics and Business. The great majority of the respondents consider the housing policy in Poland to need changes, mainly in the direction of greater support for young people, lowering the costs of mortgage loans and reducing the formalities associated with the construction of real estate. Moreover, research indicates that less than half of young people are able to fully support themselves during or shortly after graduation. Only one in four respondents had a disposable income of more than PLN 2,000. Many students and graduates plan to purchase real estate in the future, and nearly 60% of respondents intend to finance the purchase with a mortgage.
In this Article the author considers the features observed in the course of valuation of special purpose real estate, i.e., the data processing centers (DPCs) and the data processing and storage centers (DPSCs) in accordance with experience accumulated by the international community and based on the recommendations contained in the International Valuation Standards as well as the RICS methodological guidelines (The Royal Institution of Chartered Surveyors (Great Britain)).
The present article is justified as currently Russia lacks a separate and uniform for all valuers or forensic experts standard or methodological recommendations in the sphere of valuation of the data processing centers and courseware is scarce. At the same time Russia, similar to other countries of the world, is witnessing a considerable growth of construction of the DPCs later involved in the stream of commerce as transacted, subject to mortgage or disputed properties.
The present article analyzes the modern approach internationally applied to value the DPCs, summarizes the author’s personal professional experience in this sphere. At the same time the author determines and provides a deep insight into the specifics of the target group or real estate, identifies and classifies its key merits which exert effect on the properties’ value. The author provides a deep insight into applying the valuation approaches and methods, together with practical recommendations as to how measure market value within the frameworks of the cost, market or income approaches. The author gives special focus to the key (income) approach, describes the details of the types of the services which are capable of forming income from DPC operating activities, examines the items of DPC operating costs and provides practical guidelines to be applied to measure the cap rate of this type of real estate.
Research on the role of Large Language Models (LLMs) in business models and services is limited. Previous studies have utilized econometric models, technical showcases, and literature reviews. However, this research is pioneering in its empirical examination of the influence of LLMs at the firm level. The study introduces a detailed taxonomy that can guide further research on the criteria for successful LLM-based business model implementation and deepen understanding of LLM-driven business transformations. Existing knowledge on this subject is sparse and general. This research offers a more detailed business model design framework based on LLM-driven transformations. This taxonomy is not only beneficial for academic research but also has practical implications. It can act as a strategic tool for businesses, offering insights and best practices. Businesses can lev-erage this taxonomy to make informed decisions about LLM initiatives, ensuring that technology in-vestments align with strategic goals.
ABSTRACT: This study aims to examine the effect of business strategies to improve the competitive advantages of small and medium enterprises (SMEs). Further, our study considers the importance of performance and innovation as mediating variables in the relationship between business strategies and competitive advantage. The sample of the study consists of 150 SMEs in the construction and real estate industry. Our findings show that business strategies have a positive impact on competitive advantage. Better business strategies improve the competitive advantage of SMEs. Further, business performance and innovation also mediate the relationship between business strategies and competitive advantages. These results provide evidence of the importance of performance and innovation to improve the competitive advantage. It is suggested that SMEs improve their performance and innovation capability to strengthen their competitive advantages.
Rapid road construction and expansion in China resulted in massive GHG emissions. The carbon emission factors of raw materials, particularly cement, have a significant influence on the calculation of GHG emissions from road construction. This study estimates GHG emissions from road construction by taking into account regional differences in cement carbon emission factors. The results indicate that (1) total GHG emissions from road construction have a “U” shape from 2009 to 2019, with the highest level being 437 million t CO<sub>2</sub>e 2009 and the lowest level being 184 million t CO<sub>2</sub>e in 2017; (2) Class-Ⅳ roads account for roughly 80% of total GHG emissions from road construction; and (3) GHG emissions from road construction are shifting from east to west regions. This is the first paper to calculate GHG emissions from road construction by taking into account both road type and cement carbon emission factors. The findings of this study could provide references for transportation agencies to better understand the impacts of road construction to climate change and improve policymaking, especially for the development of road construction technologies and raw material production technologies.
Background: The Central Bank of Nigeria has itemized Nigerian real Gross Domestic Product (GDP) into the following: crop production, livestock, forestry, fishing, crude petroleum and natural gas, solid minerals, manufacturing, construction, trade, transport, information and communication, utilities, accommodation and food services, finance and insurance, real estate, professional, scientific and technical services, administrative, support and business services, public administration, education, human health and social services, arts, entertainment and recreation and other services.
Objective: This paper is an attempt to break Nigerian real GDP into clusters on the basis of their contributions in the year 2018 and the first three quarters of 2019.
Methodology: The researcher made use of a combination of two distances: (i) the Pearson’s (correlation coefficient) distance and (ii) the absolute correlation coefficient distance to assist into breaking Nigeria’s GDP into clusters. Additionally, two linkage methods were applied. They are: (i) The complete linkage method and (ii) the centroid linkage method. The combination of the correlation coefficient distance and the centroid linkage method was chosen because it isolated the mainstay of the Nigerian economy namely, crude oil into a cluster of its own.
Results: The three clusters which emerged were: Cluster 1: crop production, livestock, forestry, solid minerals, manufacturing, trade, transportation, utilities, real estate, professional, scientific and technical services, administrative, support and business services, public administration, education, human health and social services. Cluster 2: fishing, construction, information and communication, finance, arts, entertainment and recreation, other services. Cluster 3: crude oil
Unique contribution: The researcher suggested the most appropriate method of classifying Nigerian GDP by clustering of the mainstay of the Nigerian economy, crude oil, into a cluster of its own.
Conclusion: A combination of Pearson’s (correlation coefficient) distance and the centroid linkage method are the most appropriate method for examining Nigerian GDP because it results in the clustering of crude oil into a cluster of its own.
Key recommendation: We recommend that the Central Bank of Nigeria should categorize Nigeria’s GDP into clusters on the basis of importance to the Nigerian economy.
Improving energy efficiency in buildings is a major priority of industrialized countries. By eliminating market asymmetries, Energy Performance Certificates (EPCs) is a potential policy instrument when it comes to promoting energy efficiency of real estate. Real estate agents have an important role in providing information about dwellings for sale on the market. The aim of this paper is to study whether the introduction of EPCs changes the asking price setting of real estate agents. We take advantage of the fact that the introduction of a mandatory energy certification system represents a quasi-natural experiment, where we have data on house price and asking price. Based on the analysis, both of a hedonic model and a fixed effect model, we provide evidence that the implementation of EPCs did not affect the price setting of real estate agents. This indicates that real estate agents either disregard EPCs as providers of new information or believe that the market is indifferent to this kind of information. Our results also indicate that there are large similarities between the effects of energy labels on the asking prices and the transaction prices.
Amin Beheshti, Boualem Benatallah, Hamid Reza Motahari-Nezhad
et al.
In modern enterprises, Business Processes (BPs) are realized over a mix of workflows, IT systems, Web services and direct collaborations of people. Accordingly, process data (i.e., BP execution data such as logs containing events, interaction messages and other process artifacts) is scattered across several systems and data sources, and increasingly show all typical properties of the Big Data. Understanding the execution of process data is challenging as key business insights remain hidden in the interactions among process entities: most objects are interconnected, forming complex, heterogeneous but often semi-structured networks. In the context of business processes, we consider the Big Data problem as a massive number of interconnected data islands from personal, shared and business data. We present a framework to model process data as graphs, i.e., Process Graph, and present abstractions to summarize the process graph and to discover concept hierarchies for entities based on both data objects and their interactions in process graphs. We present a language, namely BP-SPARQL, for the explorative querying and understanding of process graphs from various user perspectives. We have implemented a scalable architecture for querying, exploration and analysis of process graphs. We report on experiments performed on both synthetic and real-world datasets that show the viability and efficiency of the approach.