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While steady-state analysis is useful, it does not consider the inherent transient characteristics of repairable systems' behavior, especially in systems that have relatively short life spans, or when their transient behavior is of special concern such as the motivating example used in this paper, military systems. Therefore, a maintenance policy that considers both transient and steady-state availability and aims to achieve the best trade-off between high steady-state availability and rapid stabilization is essential.

This paper studies the transient behavior of system availability under the Kijima Type II virtual age model. While such systems achieve steady-state availability, and it has been proved that deploying preventive maintenance (PM) can significantly improve its steady-state availability, this improvement often comes at the price of longer and increased fluctuating transient behavior, which affects overall system performance. The authors present a methodology that identifies the optimal PM policy that achieves the best trade-off between high steady-state availability and rapid stabilization based on cost-availability analysis.

When the proposed simulation-based optimization and cost analysis methodology is applied to the motivating example, it produces an optimal PM policy that achieves an availability–variability balance between transient and steady-state system behaviors. The optimal PM policy produces a notably lower availability coefficient of variation (by 11.5%), while at the same time suffering a negligible limiting availability loss of only 0.3%. The new optimal PM policy also provides cost savings of about 5% in total maintenance cost. The performed sensitivity analysis shows that the system's optimal maintenance cost is sensitive to the repair time, the shape parameter of the Weibull distribution and the downtime cost, but is robust with respect to changes in the remaining parameters.

Most of the current maintenance models emphasize the steady-state behavior of availability and neglect its transient behavior. For some systems, using steady-state availability as the sole metric for performance is not adequate, especially in systems that have relatively short life spans or when their transient behavior affects the overall performance. However, little work has been done on the transient analysis of such systems. In this paper, the authors aim to fill this gap by emphasizing such systems and applications where transient behavior is of critical importance to efficiently optimize system performance. The authors use military systems as a motivating example.

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.

The study aims to drive conceptual clarity around resistance to information technology projects, integrating multiple facets of the phenomenon from earlier studies.

The study conducts a meta-synthesis of qualitative studies on resistance to technology projects; it analyzes those studies at a case-specific level, compares and contrasts emergent concepts against each other, and “translates” those to the rest of the studies. The study uses the seven-step meta-ethnography method by Noblit and Hare to reciprocally translate emergent concepts to construct the conceptual model.

Through meta-synthesis, the study derives a new conceptual model for resistance to information technology projects, exemplifying how the identified antecedents create user resistance and how the phenomenon progresses within organizations.

This study enriches the observations and conclusions of past individual studies while explicating various facets of the mechanisms that generate and progress technology resistance within organizations. It offers fresh insights into the equivocal nature of the phenomenon and the distinctive ways it progresses from individual to group level.

Many ambitious and costly digital transformation efforts do not succeed due to user resistance. Understanding the mechanisms that create user resistance can help organizations manage technology projects better, thereby reducing the technology assimilation gap and protecting returns on related investments.

There have been extensive studies on technology acceptance (enablers) within organizations, while those relating to technology inhibitors are somewhat limited. However, the symmetry of understanding between enablers and inhibitors is vital for organizations to assimilate promising technologies and transform their business models. This model uses a new lens of sensemaking theory to explain how the antecedents trigger perceived threats and resistance behavior; it highlights the nuances around the development of resistance within individuals and its progression to groups. The resultant model offers better generalizability in organizational contexts.

Using a GED-GARCH model to estimate monthly data from January 1990 to February 2022, we test whether gold acts as a hedge or safe haven asset in 10 countries. With a downturn of the stock market, gold can be viewed as a hedge and safe haven asset in the G7 countries. In the case of inflation, gold acts as a hedge and safe haven asset in the United States, United Kingdom, Canada, China, and Indonesia. For currency depreciation, oil price shock, economic policy uncertainty, and US volatility spillover, evidence finds that gold acts as a hedge and safe haven for all countries.

Question answering (QA) answers the questions asked by people in the form of natural language. In the QA, due to the subjectivity of users, the questions they query have different expressions, which increases the difficulty of text retrieval. Therefore, the purpose of this paper is to explore new query rewriting method for QA that integrates multiple related questions (RQs) to form an optimal question. Moreover, it is important to generate a new dataset of the original query (OQ) with multiple RQs.

This study collects a new dataset SQuAD_extend by crawling the QA community and uses word-graph to model the collected OQs. Next, Beam search finds the best path to get the best question. To deeply represent the features of the question, pretrained model BERT is used to model sentences.

The experimental results show three outstanding findings. (1) The quality of the answers is better after adding the RQs of the OQs. (2) The word-graph that is used to model the problem and choose the optimal path is conducive to finding the best question. (3) Finally, BERT can deeply characterize the semantics of the exact problem.

The proposed method can use word-graph to construct multiple questions and select the optimal path for rewriting the question, and the quality of answers is better than the baseline. In practice, the research results can help guide users to clarify their query intentions and finally achieve the best answer.

Contemporary technological disruptions are espoused as though they stimulate sustainable growth in the built environment through the Green Internet of Things (G-IoT). Learning from discipline-specific experiences, this paper articulates recent advancements in the knowledge and concepts of G-IoT in relation to the construction and smart city sectors. It provides a scoping review for G-IoT as an overlooked dimension. Attention was paid to modern circularity, cleaner production and sustainability as key benefits of G-IoT adoption in line with the United Nations’ Sustainable Development Goals (UN-SDGs). In addition, this study also investigates the current application and adoption strategies of G-IoT.

This study uses the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) review approach. Resources are drawn from Scopus and Web of Science repositories using apt search strings that reflect applications of G-IoT in the built environment in relation to construction management, urban planning, societies and infrastructure. Thematic analysis was used to analyze pertinent themes in the retrieved articles.

G-IoT is an overlooked dimension in construction and smart cities so far. Thirty-three scholarly articles were reviewed from a total of 82 articles retrieved, from which five themes were identified: G-IoT in buildings, computing, sustainability, waste management and tracking and monitoring. Among other applications, findings show that G-IoT is prominent in smart urban services, healthcare, traffic management, green computing, environmental protection, site safety and waste management. Applicable strategies to hasten adoption include raising awareness, financial incentives, dedicated work approaches, G-IoT technologies and purposeful capacity building among stakeholders. The future of G-IoT in construction and smart city research is in smart drones, building information modeling, digital twins, 3D printing, green computing, robotics and policies that incentivize adoption.

This study adds to the normative literature on envisioning potential strategies for adoption and the future of G-IoT in construction and smart cities as an overlooked dimension. No previous study to date has reviewed pertinent literature in this area, intending to investigate the current applications, adoption strategies and future direction of G-IoT in construction and smart cities. Researchers can expand on the current study by exploring the identified G-IoT applications and adoption strategies in detail, and practitioners can develop implementation policies, regulations and guidelines for holistic G-IoT adoption.

The purpose of this study is to extend the Fraud Diamond Theory to celebrity Special Purpose Acquisition Companies (SPACs) and investigate their post-Initial Public Offering (IPO) stock market performance.

After obtaining a sample of celebrity SPACs from the Spacresearch.com database, fraud risk characteristics were obtained from Lexis Nexus searches. Buy and hold abnormal returns were calculated for celebrity SPACs versus a small-cap equity benchmark for time intervals after IPO, and multiple regression analysis was performed to examine the relationship between fraud risk features and post-IPO returns.

Celebrity SPACs exhibit Fraud Diamond characteristics and significantly underperform a small-cap stock portfolio on a risk-adjusted basis after IPO.

This study only examines celebrity SPACs that conducted IPOs on the NYSE and NASDAQ/AMEX and does not include those that are traded on the Over the Counter Bulletin Board (OTCBB).

Celebrity endorsement of SPAC vehicles attracts investors who may not be properly informed regarding the risk characteristics of SPACs. Accordingly, investors should be warned that celebrity SPACs underperform a small-cap equity portfolio and exhibit significant elements of fraud risk.

The use of celebrity endorsement as a marketing device to attract investment in SPACs has regulatory implications.

To the best of the authors’ knowledge, this paper is the first to examine the fraud risk characteristics and post-IPO performance of celebrity SPACs.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.

This study aims to provide an overview of the evolution of knowledge on smart destinations and identify the key issues addressed in the smart destination research domain, the main themes and sub-themes and research gaps in smart destination research as well as the future research agenda to address those gaps.

A bibliometric analysis of 409 studies published on smart destinations literature was conducted using the Bibliometrix R-package to provide a comprehensive review of studies published on smart destinations and identify the main research themes, gaps in the literature and future research opportunities.

Based on the research findings, a conceptual model of smart destination research has been proposed. This conceptual model can serve as a foundation for further knowledge generation in this research area. The findings also shed light on future research directions, highlighting research opportunities for the exploitation of cutting-edge innovations and digitalization across various dimensions of smart destinations.

Although past research has paid attention to the theme of smart destinations, this work advances scientific knowledge by providing a foundation for a new smart destination management paradigm focusing on accessibility, sustainability, digitization, experience co-creation and creativity as milestones.

本研究旨在提供关于智慧目的地知识演变的概览, 并确定智慧目的地研究领域中涉及的关键问题、主要主题和子主题, 以及智慧目的地研究的研究空白, 同时提出未来研究议程以填补这些空白。

本研究利用 Bibliometrix R 软件对发表的409篇智慧目的地文献进行了文献计量分析, 以全面回顾关于智慧目的地的研究, 并确定主要研究主题、文献中的空白以及未来的研究机会。

基于研究结果, 本研究提出了智慧目的地研究的概念模型。该概念模型可作为进一步知识生成的基础。研究发现还为未来研究方向提供了启示, 强调了利用先进创新和数字化技术在智慧目的地各个维度上进行研究的机会。

尽管过去的研究关注了智慧目的地的主题, 但本研究通过提出以可达性、可持续性、数字化、体验共创和创意为里程碑的新智慧目的地管理范式, 推进了科学知识的发展。

In this paper, the authors review the literature on the nature of the small and medium-sized enterprise concept. The review examines the broad diversity of terms and definitions used to describe these kinds of firms in scholarly and practical settings. They relate this examination to the concept of small business for the purpose of comparison, in order to highlight differences and similarities between the concepts.

Relevant literature including articles from academia and defining documents from practical settings was identified through a scope literature review. Field data were subsequently collected via questionnaires sent to editors and authors of articles related to the theme. The data were content analyzed and the resulting codes consolidated into dimensions in accordance with the Gioia method. Chi-squared tests were applied to categorical data.

The use of the composite category “small and medium” was found to be predominant in the labeling of small businesses in scientific articles, including those in journals that specialize in small businesses, with no justifications presented for this, characterizing a widespread and consensual practice between authors and editors. In the defining documents of practical settings, however, the authors observed greater consistency and precision both in the terms used and in the delimiting values for a small business (self-employed, micro business, small business). In the sample of 27 defining documents mentioned in the articles, 25 specifically defined “small business” and 20 defined “micro business,” using indicators such as number of employees and annual turnover. The indicators delimiting values regarding the category of micro business were the same in all the documents analyzed and, regarding the category of small business, many documents used the same delimiting values.

Recognizing the “non-large enterprise” myth will provide a more effective posture for editors and authors to avoid using the term “small and medium,” resulting in greater precision, understanding and knowledge regarding small businesses. A better definition of a small business by academia can help public policymakers and managers of organizations that support small businesses to tailor their actions better according to the different sizes of companies. This will also lead to social gains, given the importance of small businesses in terms of job creation and countries' economies.

The authors identified and described the myth of the “non-large enterprise” among academics, characterized by the dichotomous view of the business universe, composed of “large enterprises” and “non-large enterprises,” the latter group being characterized by the widespread use of the term “small and medium.”