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To answer the questions: what roles windows of opportunity act in the catchup process of latecomers, what strategies latecomer enterprises should adopt to size windows of opportunity to catch-up with incumbents even going beyond?

This paper studies the catch-up history of the Chinese mobile phone industry and proposes a sectoral innovation system under scenario of technology paradigm shifts. Then a history-friendly simulation model and counterfactual analysis are conducted to learn how different windows of opportunity and catch-up strategies influence the catch-up performance of latecomers.

Results show latecomers can catch up with technology ability by utilizing technology window and path-creating strategy. However, catching up with the market is not guaranteed. Demand window can help latecomers to catch up with market as it increases their survival rates, different sized windows benefit different strategies. However, it also enlarges incumbents' scale effect. Without technology window technology catch up is not guaranteed. Two windows have combination effects. Demand window affects the “degree” of change in survival rates, while the technology window affects the “speed” of change. Demand window provides security; technology window provides the possibility of a breakthrough for technology ability.

The findings of this paper provide theoretical guidance for latecomer enterprises to choose appropriate catch-up strategies to seize different opportunity windows.

This paper emphasizes the abrupt change of industrial innovation system caused by technology paradigm shifts, which makes up for the shortcomings of previous researches on industrial innovation system which either studied the influence of static factors or based on the influence of continuous changes.

Given the expansion of cities and urbanization, developing efficient and reliable transportation infrastructure, especially urban tunnels, is essential. Failure to maintain such complex construction facilities with intelligent equipment systems could result in human losses and impose huge costs on governments. Therefore, it is necessary to have practical maintenance plans and operational safety monitoring for urban tunnels, which leads to their long lifespan, increases users’ safety and reduces operation risks.

Hence, this research aims to evaluate the maintenance risks of urban tunnel lighting systems (UTLS) using a hybrid risk-based maintenance (RBM) approach. In this vein, three rounds of a fuzzy Delphi survey were conducted to consolidate the specific operation criteria and maintenance risk factors to the circumstances of Iran and UTLS. Furthermore, the fuzzy DEMATEL method was applied to determine the cause-and-effect relationships among the identified critical operation criteria. The identified risks associated with maintenance in UTLS were then analyzed and ranked using a combination of fuzzy ANP-VIKOR techniques.

The ranking of the various risks revealed that the “poor performance of switchboards in power supply due to faults in switchboard equipment” risk was ranked first, followed by the “poor performance of panels in the power supply due to unfavorable environmental conditions,” “The poor performance of panels in the power supply due to problems with switches (key failure)” and “The poor performance of panels in power supply due to burning fuses due to unauthorized current” risks. The findings of this study indicate that this hybrid maintenance method, developed as a risk-based network, provides reliability for maintaining urban tunnel lighting systems (UTLS).

It is anticipated that the findings of this research will considerably contribute to improving UTLS maintenance management while enhancing different stakeholders’ understanding of the most critical risks in maintenance, particularly toward the UTLS in Iran. An RBM management program can result in preparing and formulating policies, comprehensive guidelines or regulations for the maintenance of urban tunnels that are recommended for future research.

This study aims to systematically review and critically assess research methods for studying aging populations in tourism and hospitality. It identifies gaps and provides recommendations for advancing methodological innovation in response to emerging research agendas.

Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a systematic review of existing literature on aging populations in tourism and hospitality. Searches were performed in major databases, including Scopus and Web of Science, to collect peer-reviewed, English articles published in Social Sciences Citation Index (SSCI)-indexed tourism and hospitality journals from 1965 to 2025. The methodologies employed in the selected studies were thoroughly analyzed.

A total of 184 articles were included in the final analysis. The review revealed that current research methods predominantly rely on surveys and interviews, with limited methodological diversity. Gaps were identified, and a conceptual model was proposed to advance cross-disciplinary integration and foster innovative methods that could more comprehensively capture the complexities of senior tourists, particularly those with health conditions.

This paper is likely the first to synthesize research methodologies within this specific context, outlining a path forward for researchers to adopt more innovative, multidisciplinary approaches. This study lays the foundation for future empirical investigations into the behaviors and experiences of senior tourists, as well as the potential role of tourism in promoting healthy aging. Additionally, it provides valuable insights for tourism marketing practitioners to better accommodate this growing segment.

This paper aims to examine how bank stress tests affect bank tax planning.

The study uses US bank stress test bank size thresholds and a regression discontinuity design to investigate the effect of the Dodd-Frank Act and the instituted bank stress tests on bank tax planning. We use different measures of tax planning, including bank-specific measures and measures of tax avoidance, tax aggressiveness, and effective tax planning from recent literature. Our regression discontinuity and difference-in-differences regression analyses include bank and year fixed-effects and lagged bank characteristics to control for potential endogeneity.

This study finds that stress tests have the unintended consequences of intensifying tax planning and increasing tax avoidance. Stress-test banks increase tax avoidance by accelerating charge-offs, net interest, and non-interest expenses. However, this increase in tax planning is not optimally maximized, leading to lower effective tax planning compared to non-stress-test banks. Banks with a substantial increase in tax avoidance under the Dodd–Frank Act tend to increase their risk, investing in high-risk-weight assets and lending in riskier loan categories. These findings are consistent with tax minimization conditions under added regulatory attention and policy uncertainty.

Literature on bank tax planning is limited. Most tax avoidance literature excludes financial institutions such as bank holding companies mainly due to differences in business practices and regulatory frameworks. This study is the first to investigate tax planning behavior among US banks. The current study thus extends the research field by examining the effect of bank transparency regulations, such as bank stress tests, on bank tax planning activities. Our findings have a direct bank policy implication. They show that stress testing has the unintended consequences of increasing tax planning activities and consequently increasing risk-taking on banks with high tax avoidance, which goes against the goals of stress testing regulations.

The number of people with special needs, including citizens and military personnel, has increased as a result of terrorist attacks and challenging conditions in Iraq and other countries. With almost 80% of the world’s amputees having below-the-knee amputations, Iraq has become a global leader in the population of amputees. Important components found in lower limb prostheses include the socket, pylon (shank), prosthetic foot and connections.

There are two types of prosthetic feet: articulated and nonarticulated. The solid ankle cushion heel foot is the nonarticulated foot that is most frequently used. The goal of this study is to use a composite filament to create a revolutionary prosthetic foot that will last longer, have better dorsiflexion and be more stable and comfortable for the user. The current study, in addition to pure polylactic acid (PLA) filament, 3D prints test items using a variety of composite filaments, such as PLA/wood, PLA/carbon fiber and PLA/marble, to accomplish this goal. The experimental step entails mechanical testing of the samples, which includes tensile testing and hardness evaluation, and material characterization by scanning electron microscopy-energy dispersive spectrometer analysis. The study also presents a novel design for the nonarticulated foot that was produced with SOLIDWORKS and put through ANSYS analysis. Three types of feet are produced using PLA, PLA/marble and carbon-covered PLA/marble materials. Furthermore, the manufactured prosthetic foot undergoes testing for dorsiflexion and fatigue.

The findings reveal that the newly designed prosthetic foot using carbon fiber-covered PLA/marble material surpasses the PLA and PLA/marble foot in terms of performance, cost-effectiveness and weight.

To the best of the author’s knowledge, this is the first study to use composite filaments not previously used, such as PLA/wood, PLA/carbon fiber and PLA/marble, to design and produce a new prosthetic foot with a longer lifespan, improved dorsiflexion, greater stability and enhanced comfort for the patient. Beside the experimental work, a numerical technique specifically the finite element method, is used to assess the mechanical behavior of the newly designed foot structure.

Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.

A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.

The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.

The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.

This study aims to develop a constructability index (CI) that can ease the construction activities in a project based on the contractors’ experience and resources. The proposed CI is a vital decision support tool that quantifies the difficulty level for the contractor to execute certain activities with the contingency of other project elements. The virtual reality (VR) technology was used to provide additional data, communicate the contingency impact of other project elements on specific activities and provide sequential execution data to the contractors. This can minimize the risk of not being able to execute various activities on time and within the budget.

The VR-based CI was developed through two steps. Step 1 was to identify the factors affecting constructability by exploring the literature and consulting local construction experts. These factors were then organized through a hierarchy of main factors and subfactors and validated by local experts through predesigned surveys. The factors were classified into VR dependent or non-VR independent, and their relative weights were calculated using the analytical hierarchy process along with their reliability, which was determined using Cronbach’s alpha approach. Step 2 was to define the attributes for the constructability factors defined in Step 1 using the Multi Attribute Utility Theory to quantify the contractor’s compliance level of these factors by giving them the appropriate score. The utility factors for the VR-independent factors were obtained through standards, literature and local surveys, and they were quantified on a 1–10 scale. However, the VR-dependent factors were given their corresponding scores using the developed VR navigation environment generated by integrating Autodesk Revit and Navisworks software. Accordingly, the CI for each activity was evaluated, and the overall CI for the project was calculated by aggregating the CIs for all activities.

The developed CI quantifies the contractor’s ability to execute construction projects and addresses the lack of communication and coordination between the various construction units in the planning phase itself. Moreover, it can resolve possible hard (physical) and soft (time) construction clashes and minimize their impacts on project schedule and budget. Among the relative weights of the identified factors, prefabrication of building components was found to have the highest effect on constructability. Furthermore, applying the developed VR-CI, a real project showed that the utility values of the main factors quantified on a ten-point scale were between 6 and 9, which means routine supervisions and monitoring are required.

Though the concepts of constructability and VR have been used in different contexts, their integration to develop a comprehensive CI for the building construction industry is a unique contribution, which has not been reported previously.

This study aims to present a model for detecting and predicting creative accounting in companies listed on the Tehran Stock Exchange (TSE).

The authors conduct this research in three stages. First, the authors review the literature to determine the dimensions, components, indicators and techniques of creative accounting. Second, the authors conduct semi-structured interviews with experts using the fuzzy Delphi technique to obtain screening and reach a consensus. Finally, the authors develop a model to predict creative accounting by classifying the financial statements of the sample companies into two groups based on the use or non-use of creative accounting techniques, measuring the indicators determined in the previous stage, running various machine learning algorithms and choosing the superior algorithm.

The results indicate the usefulness of accounting information for detecting and predicting creative accounting and the relevance of several financial attributes as important predictors. The results also indicate the superiority of extremely randomized trees over other algorithms in predicting creative accounting and suggest that the primary purpose of creative accounting in Iran is earnings management. Contrary to the political cost hypothesis, large Iranian companies use creative accounting to inflate profits.

The present research also has several limitations that must be considered, and caution must be exercised in interpreting and generalizing the findings as specified in the revised manuscript.

This study’s implications are significant for policymakers, standard-setters and practitioners. By recognizing the detrimental effects of creative accounting on financial transparency within companies, policymakers can address existing gaps in accounting standards to minimize the potential for earnings manipulation. Consequently, strengthening internal and external mechanisms related to a firm’s financial performance becomes achievable. The study provides evidence of the need for audit firms to recognize the importance of creative accounting and consider creative accounting in their audit plans to prevent insufficient or even misleading disclosure by companies that extensively use creative accounting practices in their financial reporting. Moreover, knowledge of creative accounting techniques can help auditors assess audit and detection risks and serve as a valuable guide for reducing audit costs and improving audit quality.

Given that creative accounting practices distort the true or real accounting results, curbing creative accounting practices reduces corporate failures and could lead to the reduction of job losses and other social consequences.

This study uses a unique database in Iran to determine a model for predicting creative accounting using a mixed-method methodology, qualitative and quantitative, to identify creative accounting techniques and run various machine learning algorithms.

As financial markets for environmentally friendly investment grow in both scope and size, analyzing the relationship between green financial markets and African stocks becomes an important issue. Therefore, this paper examines the role of infectious disease-based uncertainty on the dynamic spillovers between African stock markets and clean energy stocks.

The authors employ the dynamic spillover in time and frequency domains and the nonparametric causality-in-quantiles approach over the period of November 30, 2010, to August 18, 2021.

These findings are discernible in this study's analysis. First, the authors find evidence of strong connectedness between the African stock markets and the clean energy market, and long-lived but weak in the short and medium investment horizons. Second, the BDS test shows that nonlinearity is crucial when examining the role of infectious disease-based equity market volatility in affecting the interactions between clean energy stocks and African stock markets. Third, the causal analysis provides evidence in support of a nonlinear causal relationship between uncertainties due to infectious diseases and the connection between both markets, mostly at lower and median quantiles.

Considering the global and recent use of clean energy equities and the stock markets for hedging and speculative purposes, one may argue that rising uncertainties may significantly influence risk transmissions across these markets. This study, therefore, is the first to examine the role of pandemic uncertainty on the connection between clean stocks and the African stock markets.

This paper aims to develop an optimal maintenance and spare parts policy for an urban micro wind power system, focusing on two urban micro wind farms (UMWF). The reliability and efficiency of these systems are sought to be enhanced by considering the relationship between urban wind parameters and wind turbine degradation.

A proportional hazards (PH) model is utilized to describe how urban wind conditions impact turbine degradation. The maintenance strategy includes preventive maintenance (PM), corrective maintenance (CM) and opportunistic maintenance (OM). A multi-objective optimization algorithm is developed to optimize the joint policy of OM plans and spare parts resource allocation.

The proposed maintenance and spare parts policy effectively balances the trade-offs between PM, CM and OM strategies. Numerical experiments demonstrate that the policy improves the reliability of UMWF, reducing downtime and maintenance costs while ensuring the availability of spare parts when needed. The results show a significant enhancement in system performance compared to traditional maintenance approaches.

A novel maintenance policy and spare parts management approach for urban micro wind power systems is proposed. A multi-objective optimization algorithm is developed to optimize the OM schedule and maintenance spare parts resource management strategy for wind farms in urban wind environments.