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Exploration and exploitation have long been documented as prominent approaches to business management and organizational adaptation to external environment. Maintaining balance between these activities is a key to survival and prosperity. However, there is little direct evidence of the effect of such combined usage of both approaches on firm performance in times of crisis, especially within small- and medium-sized enterprises (SMEs). The purpose of this paper is to reveal the role of balanced ambidexterity in shaping firm performance during COVID-19 recession.

Based on a survey of 333 Russian SMEs, the authors test the proposed theoretical framework linking innovative ambidexterity to firm performance level and variability taking into account technological uncertainty.

The results show that innovative ambidexterity tends to increase level and decrease variability of performance outcomes, whereas technological uncertainty acts as a positive contingency for this impact.

The results provide an improved understanding of ambidexterity and organizational literatures by clarifying the contingent nature of the ambidexterity–firm performance relationship during COVID-19 recession.

Distribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.

The problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.

For the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.

The proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

This survey explores the application of real options theory to the field of health economics. The integration of options theory offers a valuable framework to address these challenges, providing insights into healthcare investments, policy analysis and patient care pathways.

This research employs the real options theory, a financial concept, to delve into health economics challenges. Through a systematic approach, three distinct models rooted in this theory are crafted and analyzed. Firstly, the study examines the value of investing in emerging health technology, factoring in future advantages, associated costs and unpredictability. The second model is patient-centric, evaluating the choice between immediate treatment switch and waiting for more clarity, while also weighing the associated risks. Lastly, the research assesses pandemic-related government policies, emphasizing the importance of delaying decisions in the face of uncertainties, thereby promoting data-driven policymaking.

Three different real options models are presented in this study to illustrate their applicability and value in aiding decision-makers. (1) The first evaluates investments in new technology, analyzing future benefits, discount rates and benefit volatility to determine investment value. (2) In the second model, a patient has the option of switching treatments now or waiting for more information before optimally switching treatments. However, waiting has its risks, such as disease progression. By modeling the potential benefits and risks of both options, and factoring in the time value, this model aids doctors and patients in making informed decisions based on a quantified assessment of potential outcomes. (3) The third model concerns pandemic policy: governments can end or prolong lockdowns. While awaiting more data on the virus might lead to economic and societal strain, the model emphasizes the economic value of deferring decisions under uncertainty.

This research provides a quantified perspective on various decisions in healthcare, from investments in new technology to treatment choices for patients to government decisions regarding pandemics. By applying real options theory, stakeholders can make more evidence-driven decisions.

Decisions about patient care pathways and pandemic policies have direct societal implications. For instance, choices regarding the prolongation or ending of lockdowns can lead to economic and societal strain.

The originality of this study lies in its application of real options theory, a concept from finance, to the realm of health economics, offering novel insights and analytical tools for decision-makers in the healthcare sector.

This paper aims to study a neural network-based fault-tolerant controller to improve the tracking control performance of an unmanned autonomous helicopter with system uncertainty, external disturbances and sensor faults, using the prescribed performance method.

To ensure that the tracking error satisfies the prescribed performance, the authors adopt an error transformation function method. A control scheme based on the neural network and high-order disturbance observer is designed to guarantee the boundedness of the closed-loop system. A simulation is performed to prove the validity of the control scheme.

The developed adaptive fault-tolerant control method makes the system with sensor fault realize tracking control. The error transformation function method can effectively handle the prescribed performance requirements. Sensor fault can be regarded as a type of system uncertainty. The uncertainty can be approximated accurately using neural networks. A high-order disturbance observer can effectively suppress compound disturbances.

The tracking performance requirements of unmanned autonomous helicopter system are considered in the design of sensor fault-tolerant control. The inequality constraint that the output tracking error must satisfy is transformed into an unconstrained problem by introducing an error transformation function. The fault state of the velocity sensor is considered as the system uncertainty, and a neural network is used to approach the total uncertainty. Neural network estimation errors and external disturbances are treated as compound disturbances, and a high-order disturbance observer is constructed to compensate for them.

This study aims to examine the impact of national culture on classification shifting in Eastern European Member States of EU Eastern European countries (EEU) vis-à-vis the Western Member States of EU (WEU). The EEU provides a unique sample to study the quality of financial reporting that the authors measure with classification shifting given that for more than five decades they were following the model of a centrally planned economy, where market-based financial reporting was absent. Yet, the EEU transitioned to a market-based economy and completed its accession to the EU.

This study uses a panel data set of firm year observations from 1996 and 2020 that covers the full transition of EEU. This empirical analysis is based on fixed effects panel regression analysis where the authors report a plethora of identifications.

This study finds classification shifting in the EEU countries since their transition to the market-based economy, though they have no long record of market-based financial reporting. This study also notices that cultural factors are associated with classification shifting across all Member States of the EU. This study further examines the impact of interactions between cultural characteristics and special items and reveal variability between WEU and EEU. As part of the robustness analysis, this study also tests the impact of culture on real earnings management measures for both WEU vs EEU, confirming the variability of the impact of culture on earnings management.

Future research could explore the role of religion differences in WEU vis-à-vis EEU states, as they are also subject to cultural differences.

The findings are important for regulators, external monitors and investors, as they show that cultural factors affect earnings management with some variability across countries in the EU, and they should be acknowledged in policymaking.

The findings show that cultural differences between EEU and the “old” Member States of the EU could explain classification shifting.

To the best of the authors’ knowledge, this is the first study that sheds light on the impact of national culture on classification shifting in EEU of EU vis-à-vis the “old” WEU of EU.

This study explores the relationship between economic freedom and gender ideologies on the allocation of women’s opportunity-to-necessity entrepreneurship across countries. Opportunity entrepreneurship is typically understood as one’s best option for work, whereas necessity entrepreneurship describes the choice as driven by no better option for work. Specifically, we examine how economic freedom (i.e. each country’s policies that facilitate voluntary exchange) and gender ideologies (i.e. each country’s propensity for gendered separate spheres) affect the distribution of women’s opportunity-to-necessity entrepreneurship across countries.

We construct our sample by matching data from the following country-level sources: the Global Entrepreneurship Monitor’s Adult Population Survey (APS), the Fraser Institute’s Economic Freedom Index (EFI), the European/World Value Survey’s Integrated Values Survey (IVS) gender equality index, and other covariates from the IVS, Varieties of Democracy (V-dem) World Bank (WB) databases. Our final sample consists of 729 observations from 109 countries between 2006 and 2018. Entrepreneurial activity motivations are measured by the ratio of the percentage of women’s opportunity-driven total nascent and early-stage entrepreneurship to the percentage of female necessity-driven total nascent and early-stage entrepreneurship at the country level. Due to a first-order autoregressive process and heteroskedastic cross-sectional dependence in our panel, we estimate a fixed-effect regression with robust standard errors clustered by country.

After controlling for multiple macro-level factors, we find two interesting findings. First, economic freedom positively affects the ratio of women’s opportunity-to-necessity entrepreneurship. We find that the size of government, sound money, and business and credit regulations play the most important role in shaping the distribution of contextual motivations over time and between countries. However, this effect appears to benefit efficiency and innovation economies more than factor economies in our sub-sample analysis. Second, gender ideologies of political equality positively affect the ratio of women’s opportunity-to-necessity entrepreneurship, and this effect is most pronounced for efficiency economies.

This study offers one critical contribution to the entrepreneurship literature by demonstrating how economic freedom and gender ideologies shape the distribution of contextual motivation for women’s entrepreneurship cross-culturally. We answer calls to better understand the variation within women’s entrepreneurship instead of comparing women’s and men’s entrepreneurial activity. As a result, our study sheds light on how structural aspects of societies shape the allocation of women’s entrepreneurial motivations through their institutional arrangements.

The primary aim of this research was to investigate the mediating effect of the decision-making structure on the relationship between perceived environmental uncertainty and hospital performance.

Online and manual survey questionnaires were used to collect data in this study. The target population of this study consists of all middle managers within 11 COVID-19 referral hospitals in Surabaya. A total of 189 responses were collected, however, 27 incomplete responses were excluded from the final dataset. Data was analyzed using SEM-PLS.

The study's findings indicate that decision-making structure plays a role in mediating the link between perceived environmental uncertainty and hospital performance assessed via the Balanced Scorecard, highlighting the significance of flexible decision-making processes during uncertain periods. Moreover, based on our supplementary test, respondents' demographic characteristics influence their perceptions of hospital performance.

Hospital administrators can consider the significance of decision-making structures in responding to environmental uncertainties like the COVID-19 pandemic. By fostering adaptable decision-making processes and empowering middle managers, hospitals may enhance their performance and resilience in challenging situations. Additionally, based on supplementary tests, it is found that differences in the perception of the three Balanced Scorecard perspectives imply that hospitals categorized as types A, B, C, and D should prioritize specific areas to improve their overall performance.

This research adds substantial originality and value to the existing body of knowledge by exploring the interplay between decision-making structures, environmental uncertainty, and hospital performance. It contributes to the literature by specifically focusing on the Covid-19 pandemic, a unique and unprecedented global crisis.

The purpose of this paper is to investigate the impact of the COVID-19 pandemic on company innovation, specifically centering on the quantity and quality of innovation. The paper aims to provide a comprehensive understanding of whether the epidemic inhibits innovation and the role of digital transformation in mitigating this negative impact.

The paper uses a quasi-experimental study of the COVID-19 pandemic and constructs a differential model to analyze the relationship between the epidemic and firm innovation in three dimensions: total, quantity and quality. The paper also uses a difference-in-difference-in-differences model to test whether digital transformation of firms mitigates the negative impact of the epidemic and its mechanism of action.

The results show that COVID-19 significantly reduced the overall level of firm innovation, primarily in terms of quantity rather than quality. Furthermore, this study finds that digital transformation plays a pivotal role in mitigating the pandemic’s adverse impact on innovation. By addressing financing constraints and countering demand insufficiency, digital transformation acts as a catalyst for preserving and fostering innovation during and after the pandemic.

This study extends the current research on the pandemic’s impact on firm innovation at the micro level. It offers valuable insights into strategies for fostering digital transformation among Chinese enterprises in the post-pandemic era.

Corporate site visits increase labor investment efficiency.

Our empirical model for the baseline analysis follows those of Jung et al. (2014) and Ghaly et al. (2020).

We show that corporate site visits are associated with significantly higher labor investment efficiency; more specifically, site visits reduce both over-hiring and under-hiring of employees. The effect of site visits on labor investment efficiency is more pronounced for firms with higher labor adjustment costs, greater financial constraints, weaker corporate governance and lower financial reporting quality. We also find that site visits mitigate labor cost stickiness.

First, while the literature has suggested how the presence of institutional investors and analysts may affect labor investment decisions, we focus on institutional investors and analysts’ activities and interactions with firm executives. We provide direct evidence that institutional investors and analysts may use corporate site visits to improve labor investment efficiency. Second, our study adds to a line of recent studies on how corporate site visits reduce information asymmetry and agency conflicts. We show that corporate site visits allow institutional investors and analysts to influence labor investment efficiency. We also provide new evidence that corporate site visits reduce labor cost stickiness.

Untimely responses to emergency situations in urban areas contribute to a rising mortality rate and impact society's primary capital. The efficient dispatch and relocation of ambulances pose operational and momentary challenges, necessitating an optimal policy based on the system's real-time status. While previous studies have addressed these concerns, limited attention has been given to the optimal allocation of technicians to respond to emergency situation and minimize overall system costs.

In this paper, a bi-objective mathematical model is proposed to maximize system coverage and enable flexible movement across bases for location, dispatch and relocation of ambulances. Ambulances relocation involves two key decisions: (1) allocating ambulances to bases after completing services and (2) deciding to change the current ambulance location among existing bases to potentially improve response times to future emergencies. The model also considers the varying capabilities of technicians for proper allocation in emergency situations.

The Augmented Epsilon-Constrained (AEC) method is employed to solve the proposed model for small-sized problem. Due to the NP-Hardness of the model, the NSGA-II and MOPSO metaheuristic algorithms are utilized to obtain efficient solutions for large-sized problems. The findings demonstrate the superiority of the MOPSO algorithm.

This study can be useful for emergency medical centers and healthcare companies in providing more effective responses to emergency situations by sending technicians and ambulances.

In this study, a two-objective mathematical model is developed for ambulance location and dispatch and solved by using the AEC method as well as the NSGA-II and MOPSO metaheuristic algorithms. The mathematical model encompasses three primary types of decision-making: (1) Allocating ambulances to bases after completing their service, (2) deciding to relocate the current ambulance among existing bases to potentially enhance response times to future emergencies and (3) considering the diverse abilities of technicians for accurate allocation to emergency situations.