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This paper investigates a problem in a reverse logistics (RLs) network to decide whether to dispose of unsold goods in primary stores or re-commercialize them in outlet centers. By deducting the costs associated with each policy from its revenue, this study aims to maximize the profit from managing unsold goods.

A new mixed-integer linear programming model has been developed to address the problem, which considers the selling prices of products in primary and secondary stores and the costs of transportation, cross-docking and returning unwanted items. As a result of uncertain nature of the cost and time parameters, gray numbers are used to deal with it. In addition, an innovative uncertain solution approach for gray programming problems is presented that considers objective function satisfaction level as an indicator of optimism.

According to the results, higher costs, including transportation, cross-docking and return costs, make sending goods to outlet centers unprofitable and more goods are disposed of in primary stores. Prices in primary and secondary stores heavily influence the number of discarded goods. Higher prices in primary stores result in more disposed of goods, while higher prices in secondary stores result in fewer. As a result of the proposed method, the objective function satisfaction level can be viewed as a measure of optimism.

An integral contribution of this study is developing a new mixed-integer linear programming model for selecting the appropriate goods for re-commercialization and choosing the best outlet center based on the products' price and total profit. Another novelty of the proposed model is considering the matching percentage of boxes with secondary stores’ desired product lists and the probability of returning goods due to non-compliance with delivery dates. Moreover, a new uncertain solution approach is developed to solve mathematical programming problems with gray parameters.

This paper aims to examine how managers make non-generally accepted accounting principles (GAAP) exclusion decisions depending on the regulatory guidance provided and their motivations. Guidance detail is a double-edged sword: resolving uncertainty but risking rule-based compliance over principled judgment.

This paper uses the context of non-GAAP measures in reporting, given the history of Securities and Exchange Commission changes in guidance detail. Drawing on theories of epistemic motivation and process accountability, this paper manipulates the goal of management (informativeness vs. opportunism) and guidance detail to examine effects on management decisions to exclude an ambiguous charge.

The 2×2 between participants experiment with 132 managers reveals that more detailed guidance increases likelihood of exclusion of an ambiguous charge. This paper further finds that this exclusion is more likely when management is given an informativeness goal, a result of a mediating effect of epistemic motivation. However, these findings only hold at low levels of process accountability.

The findings regarding the psychological concepts recognize the influence of perceived decision uncertainty by suggesting how managers respond to the level of regulatory guidance detail, offering regulators and auditors a basis for understanding and anticipating managerial reporting choices. Also, awareness of heightened epistemic motivation under the informativeness goal provides a nuanced practical understanding of non-GAAP decision drivers. Finally, the finding that effects are more pronounced for managers with lower process accountability highlights the significance of organizational accountability structures in guiding managerial choices, which can inform board-level governance and control decisions.

Pragmatically, this paper finds that detailed guidance leads to more appropriate exclusion decisions under a goal of informativeness but finds no such evidence where the goal is opportunism. No prior study has examined how the level of detail in guidance affects managers’ disclosure choices.

We aim to clarify the effect of economic uncertainty on Chinese stock market fluctuations. We extend the understanding of the asymmetric connectedness between economic uncertainty and stock market risk and provide different characteristics of spillovers from economic uncertainty to both upside and downside risk. Furthermore, we aim to provide the different impact patterns of stock market volatility following several exogenous shocks.

We construct a Chinese economic uncertainty index using a Factor-Augmented Variable Auto-Regressive Stochastic Volatility (FAVAR-SV) model for high-dimensional data. We then examine the asymmetric impact of realized volatility and economic uncertainty on the long-term volatility components of the stock market through the asymmetric Generalized Autoregressive Conditional Heteroskedasticity-Mixed Data Sampling (GARCH-MIDAS) model.

Negative news, including negative return-related volatility and higher economic uncertainty, has a greater impact on the long-term volatility components than positive news. During the financial crisis of 2008, economic uncertainty and realized volatility had a significant impact on long-term volatility components but did not constitute long-term volatility components during the 2015 A-share stock market crash and the 2020 COVID-19 pandemic. The two-factor asymmetric GARCH-MIDAS model outperformed the other two models in terms of explanatory power, fitting ability and out-of-sample forecasting ability for the long-term volatility component.

Many GARCH series models can also combine the GARCH series model with the MIDAS method, including but not limited to Exponential GARCH (EGARCH) and Threshold GARCH (TGARCH). These diverse models may exhibit distinct reactions to economic uncertainty. Consequently, further research should be undertaken to juxtapose alternative models for assessing the stock market response.

Our conclusions have important implications for stakeholders, including policymakers, market regulators and investors, to promote market stability. Understanding the asymmetric shock arising from economic uncertainty on volatility enables market participants to assess the potential repercussions of negative news, engage in timely and effective volatility prediction, implement risk management strategies and offer a reference for financial regulators to preemptively address and mitigate systemic financial risks.

First, in the face of domestic and international uncertainties and challenges, policymakers must increase communication with the market and improve policy transparency to effectively guide market expectations. Second, stock market authorities should improve the basic regulatory system of the capital market and optimize investor structure. Third, investors should gradually shift to long-term value investment concepts and jointly promote market stability.

This study offers a novel perspective on incorporating a Chinese economic uncertainty index constructed by a high-dimensional FAVAR-SV model into the asymmetric GARCH-MIDAS model.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.

Logistics service provider (LSP) selection involves multiple criteria, alternatives and decision makers. Group decision-making involves vagueness and uncertainty. This paper aims to propose a novel fuzzy method for assessing and selecting agile, resilient and sustainable LSP, taking care of the inconsistency and uncertainty in subjective group ratings.

Eighteen agile, resilient, operational, economic, environmental and social sustainability criteria were identified from the literature and discussion with experts. Interval-valued Fermatean fuzzy (IVFF) sets are more flexible and accurate for handling complex uncertainty, impreciseness and inconsistency in group ratings. The IVFF PIvot Pairwise RElative Criteria Importance Assessment Simplified (IVFF-PIPRECIAS) and IVFF weighted aggregated sum product assessment (IVFF-WASPAS) methods are applied to determine criteria weights and LSP evaluation, respectively.

Collaboration and partnership, range of services, capacity flexibility, geographic coverage, cost of service and environmental safeguard are found to have a greater influence on the LSP selection, as per this study. The LSP (L3) with the highest score (0.949) is the best agile, resilient and sustainable LSP in the manufacturing industry.

Hybrid IVFF-based PIPRECIAS and WASPAS methods are proposed for the selection of agile, resilient and sustainable LSP in the manufacturing industry.

The model can help supply chain managers in the manufacturing industry to easily adopt the hybrid model for agile, resilient and sustainable LSP selection.

The paper also contributes to the social sustainability of logistics workers.

To the best of the authors’ knowledge, IVFF-PIPRECIAS and IVFF-WASPAS methods are applied for the first time to select the best agile, resilient and sustainable LSP in a developing economy context.

Entrepreneurial action under uncertainty has captured the interest of scholars and practitioners alike. However, this growing body of research has yet to connect entrepreneurial action with actual actions of entrepreneurs. We combine insights from effectuation theory and the psychology of entrepreneurship to investigate drivers of entrepreneurial actions involved in starting and running ventures, particularly optimism, self-efficacy and the use of causal and effectual logics.

The study employs a unique mixed-method approach combining a survey with experience sampling data. After measuring demographics, preference for effectual or causal logics and psychological variables, the 197 US entrepreneurs sampled entrepreneurs logged their daily actions in the form of asks during 60 consecutive days.

Results suggest that self-efficacy and causal logics are key drivers of entrepreneurial actions, while optimism serves as a deterrent. Interestingly, the impact of self-efficacy on actions is moderated by the entrepreneur’s experience level, measured both in years and past asking experience.

The potential of the ask as the basic mechanism for entrepreneurial actions is explored. Based on the findings, the authors offer new and fertile insights by linking psychological traits to entrepreneurial actions, causal and effectual logics and entrepreneurial expertise development.

Research on corporate social responsibility (CSR) within the real estate sector is limited, and the precise workings of its impact are still unclear. Under the premise that real estate enterprises face environmental uncertainty in China, this study explored the impact of CSR on real estate enterprise value.

To investigate the impact of CSR on enterprise value, we studied 111 real estate enterprises with A-shares listed on Shanghai and Shenzhen stock exchanges from 2010 to 2020, and performed empirical tests to determine the moderating effect of environmental uncertainty on this relationship.

(1) The fulfillment of corporate social responsibility (CSR) significantly influences the value of real estate enterprises. A sub-dimensional analysis reveals that fulfilling stakeholder and social welfare responsibilities within CSR positively impacts enterprise value, whereas environmental responsibility does not exert a notable effect. (2) The uncertainty associated with environmental changes profoundly affects the relationship between CSR and the value of real estate enterprises. More precisely, as environmental uncertainty increases, it amplifies the beneficial impact of CSR on enterprise value.

These findings are valuable for real estate enterprises as they navigate the transition towards sustainable development, and they also provide insight for the government in formulating policies aimed at regulating the real estate sector.

This study complements the existing discussion and research on corporate social responsibility (CSR) and enterprise value in the real estate industry, while elucidating the underlying mechanism of how environmental uncertainty mediates the relationship between the two.

This study aims to investigate the impact of green marketing strategy (GMS) and firms’ decision to invest in or divest from green marketing activities during a crisis on business performance.

The study collected survey data from 245 Greek firms during the 2015 Eurozone crisis to investigate the impact of GMS and green marketing investments on firm resilience during crisis. Time-lagged, objective performance data for a subset of these firms helped examine the impact of GMS on postcrisis financial performance.

Pursuing a GMS builds resilience, especially for companies that decided not to reduce resources allocated to green marketing activities during a recession. Beyond resilience, firms investing in GMS during the crisis experienced improved financial performance in the long run. Finally, this research proposes a typology of GMS responses during a crisis.

This study does not specify which types of green marketing activities lead to more investment or divestment during a crisis.

The study offers insights for allocating resources to green marketing during recessions. Supporting GMSs during unpredictable times is important to successfully navigate performance both during and after a crisis. Six crisis response profiles are offered: green-nonbelievers, dis-investors, reluctants and cautious-, opportunistic- and strategic-green investors.

The study proposes a balanced approach to environmental sustainability, marketing strategy and firm performance during a crisis.

The study argues that GMSs enable firms to survive a crisis and recover from financial shocks.

Multi-criteria methods represent an adequate tool for solving complex decision problems that provide real support to the decision maker in the choice process. This paper analyzes a decision problem that recurs over time using one of the newer methods as the Parsimonious AHP.

In this paper we integrated the P-AHP with: (1) the weighted average which takes into account the objectivity of the data; (2) ordered weighted average (OWA) aggregation operators that address the subjective nature of the data; (3) the Choquet integral and (4) the Sugeno integral which also considers the uncertain nature of the final ranking as it is defined on a fuzzy measure.

The present paper proves that variations in the final ranking, due to the different mathematical properties of the selected aggregators, are fundamental to select the best alternative without neglecting any characteristic of the input data. In fact, it is discussed and underlined how and why the best alternative is one that never excels but has very good positions with respect to all aggregation operator rankings.

The aim and innovation presented in this work is the use of the Parsimonious AHP (P-AHP) method in a dynamic way with the use of different aggregation techniques.

The paper aims to propose an innovative two-stage decision model to address the sustainable-resilient supplier selection and order allocation (SSOA) problem in the single-valued neutrosophic (SVN) environment.

First, the sustainable and resilient performances of suppliers are evaluated by the proposed integrated SVN-base-criterion method (BCM)-an acronym in Portuguese of interactive and multi-criteria decision-making (TODIM) method, with consideration of the uncertainty in the decision-making process. Then, a novel multi-objective optimization model is formulated, and the best sustainable-resilient order allocation solution is found using the U-NSGA-III algorithm and TOPSIS method. Finally, based on a real-life case in the automotive manufacturing industry, experiments are conducted to demonstrate the application of the proposed two-stage decision model.

The paper provides an effective decision tool for the SSOA process in an uncertain environment. The proposed SVN-BCM-TODIM approach can effectively handle the uncertainties from the decision-maker’s confidence degree and incomplete decision information and evaluate suppliers’ performance in different dimensions while avoiding the compensatory effect between criteria. Moreover, the proposed order allocation model proposes an original way to improve sustainable-resilient procurement values.

The paper provides a supplier selection process that can effectively integrate sustainability and resilience evaluation in an uncertain environment and develops a sustainable-resilient procurement optimization model.