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Despite the abundance of small-scale farms in the USA and their importance for both rural economic development and food availability, the extensive research on small business management and entrepreneurship has mostly neglected the agricultural context, leaving many of these farms' business challenges unexplored. The authors focus on informing a specific decision faced by small farm managers: selling directly to consumers (i.e. farmer's markets) versus selling through aggregators. By collecting historical data and a series of interviews with industry experts, the authors employ simulation methodology to offer a framework that advises how small-scale farmers can allocate their product across these two channels to increase revenue in a given season. The results, which are relevant for operations management, small business management and entrepreneurship literature, can help small-scale farmers improve their performance and compete against their larger counterparts.

The authors rely on historical and interview data from key industry players (an aggregator and a small farm manager) to design a simulation analysis that determines which factors influence season-long farm revenue performance under varying strategies of channel allocation and commodity production.

The model suggests that farm managers should plan to evenly split their production between the two distribution channels, but if an even split is not possible, they should plan to keep a larger percentage in the nonaggregator (farmers' market/direct) channel. Further, the authors find that farmers can benefit significantly from a strong aggregator channel customer base, which suggests that farmers should promote and advertise the aggregator channel even if they only use it for a limited amount of their product.

The authors integrate small business management and operations management literature to study a widely understudied context and present practical implications for the performance of small-scale farms.

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.

Blood availability is critical for saving lives in various healthcare services. Ensuring blood availability can only be achieved through efficient management of the blood supply chain (BSC). A key component of the BSC is bloodmobiles, which are responsible for a significant portion of blood donation collections. The most crucial factor affecting the efficacy of bloodmobiles is their location selection. Therefore, detailed decision analyses are essential for the location selection of bloodmobiles. This study proposes a comprehensive approach to bloodmobile location selection for resilient BSCs.

This study provides a novel integration of the spherical fuzzy analytical hierarchy process (SF-AHP) and spherical fuzzy complex proportional assessment (SF-COPRAS) methodologies. In this framework, the criteria are weighted using SF-AHP. The alternatives are then evaluated using SF-COPRAS, employing criteria weights obtained from SF-AHP without defuzzification.

The results show that supply conditions and resilience are the most important criteria for a bloodmobile location selection. Additionally, the validation analyses confirm the stability of the solution.

This study presents several managerial implications that can aid mid-level managers in the BSC during the decision-making process for bloodmobile location selection. The critical factors revealed, along with their importance in choosing bloodmobile locations, serve as a comprehensive guide. Additionally, the framework proposed in this study offers decision-makers (DMs) an effective method for ranking potential bloodmobile locations.

This study presents the first application of multi-criteria decision-making (MCDM) for bloodmobile location selection. In this manner, several aspects of bloodmobile location selection are considered for the first time in the existing literature. Furthermore, from the methodological aspect, this study provides a novel SF-AHP-integrated SF-COPRAS methodology.

The present study aims to provide a roadmap for meeting the carbon-free, green energy production target within the stipulated period while also considering climate targets through a sustainable auctioning scheme.

The research outlines the opportunity to design auctions based on qualitative research, the impact of auctions on energy costs and thus the feasibility of suggested auctioning schemes based on country-specific empirical evidence and benefits.

The conclusions show that this may result in various advantages for emerging economies relating to technology-neutral site-specific auctions if designed according to state-specific socio-economic conditions.

The planned addition to the state-of-the-art in the renewable energy (RE) field of this paper is that it intends to bridge the gap between theory and practice. The analysis has concepts for research, practice and/or community. Thus, it can serve as a primary source of literature reference for those willing to learn more about the aspects of cost related to RE.

This study investigated the relationship between last-mile distribution or delivery (LMD) and commodity access through the mediating role of commodity availability and commodity security and the moderating effect of supply chain integration (SCI).

The study adopted the survey research design and employed the questionnaire instrument in collecting primary data from respondents in Eastern Regional Health Institutions in Ghana. The total number of valid responses received was 204. The partial least squares structural equation modeling (PLS-SEM) approach was adopted to analyze the relationship between the study variables.

The findings showed that there is a positive and significant relationship between LMD and commodity availability as well as LMD and commodity security. Moreover, while the relationship between commodity availability and commodity access is positive and significant, that between commodity security and commodity access is positive but insignificant. Furthermore, there is a positive and statistically significant relationship between LMD and commodity access. The study discovered that the interaction between LMD and commodity access is insignificant and negatively affected by SCI.

To the best of the authors' knowledge, no previous studies have empirically verified the effect of LMD on commodity access in the presence of mediating factors such as commodity availability and commodity security and SCI as the moderating factors.

The paper proposes a two-level closed-loop supply chain (CLSC) dynamic competitive model based on different competitive cooperation situations, and explores the impact of competitive cooperation methods on the pricing strategies, recycling and remanufacturing strategies and competitive model selection strategies of supply chain firms.

This paper establishes a CLSC game consisting of a manufacturer and two retailers. Firstly, five CLSC models are established in both horizontal and vertical dimensions, each of which competes with one another. Secondly, the recycling and remanufacturing pricing strategies are analyzed under different competition or cooperation models. Finally, the results are verified through numerical analysis.

The overall profitability of the CLSC is highest when the manufacturer–retailer partnership alliance is in place. The relationship between retailers and manufacturers is also found to be the best way to achieve overall optimization of the CLSC.

The paper investigates the relationship between the competitive partnership and the total profit of the CLSC, taking into account how to optimize the overall benefit, and focusing on how to optimize the individual interests of each participating enterprise. The results can provide basis and guidance for managers' pricing decision and competition cooperation.

In the context of innovation-driven development strategy, open innovation has become an important way for enterprises to gain competitive advantages on the path of innovation and development. However, with the increasing competition, enterprises' open innovation is restricted by some constraints. How to promote open innovation in the restricted situations has become an existing research gap. Based on the perspective of digital transformation, this paper discusses how to promote the open innovation of enterprises under the restricted situations and find its breakthrough path, and analyzes the moderating effect of innovation persistence and political relevance.

Due to the complexity and confidentiality of military–civilian integration enterprises, they have become typical innovation-restricted enterprises. In this study, it selects a-share listed companies in the field of military–civilian integration in China in 2016–2020 as the research sample, and uses the two-way fixed-effect model to analyze the proposed variables. Finally, the robustness of the results in this paper is verified by a series of robustness tests and endogeneity tests.

The results show that digital transformation facilitates open innovation in military–civil integration enterprises, and that innovation persistence and political relevance positively moderate the relationship between the two. Further, digital transformation can promote open innovation in military–civil integration enterprises by easing the financing constraints and reducing information asymmetry. Innovation persistence has a more pronounced positive moderating effect among civilian-to-military and SMEs, and digital transformation of firms in the South has a negative effect on open innovation, but innovation persistence and political relevance dampen this negative effect.

Previous studies on the restrictions of open innovation or its dark side are mostly case studies and qualitative research. In contrast, the superiority and novelty of this study is in the form of a typical innovation-restricted enterprises “civil-military integration enterprise” as the research sample, based on the perspective of digital transformation, through empirical analysis method to explore how to better implementation of open innovation in the restricted situations. The findings of the study can not only enrich the application of digital transformation and open innovation theory, but also provide practical guidance for military–civil integration innovation in restricted situations.

Aiming to resolve cross-cultural paradoxes in combining artificial intelligence (AI) with human intelligence (HI) for international humanitarian logistics, this paper aims to adopt an unorthodox Yin–Yang dialectic approach to address how AI–HI interactions can be interpreted as a sophisticated cross-cultural knowledge creation (KC) system that enables more effective decision-making for providing humanitarian relief across borders.

This paper is conceptual and pragmatic in nature, whereas its structure design follows the requirements of a real impact study.

Based on experimental information and logical reasoning, the authors first identify three critical cross-cultural challenges in AI–HI collaboration: paradoxes of building a cross-cultural KC system, paradoxes of integrative AI and HI in moral judgement and paradoxes of processing moral-related information with emotions in AI–HI collaboration. Then applying the Yin–Yang dialectic to interpret Klir’s epistemological frame (1993), the authors propose an unconventional stratified system of cross-cultural KC for understanding integrative AI–HI decision-making for humanitarian logistics across cultures.

This paper aids not only in deeply understanding complex issues stemming from human emotions and cultural cognitions in the context of cross-border humanitarian logistics, but also equips culturally-diverse stakeholders to effectively navigate these challenges and their potential ramifications. It enhances the decision-making process and optimizes the synergy between AI and HI for cross-cultural humanitarian logistics.

The originality lies in the use of a cognitive methodology of the Yin–Yang dialectic to metaphorize the dynamic genesis of integrative AI-HI KC for international humanitarian logistics. Based on system science and knowledge management, this paper applies game theory, multi-objective optimization and Markov decision process to operationalize the conceptual framework in the context of cross-cultural humanitarian logistics.

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.

This study aims to explore the impact of decentralized long-term rental apartments on the value of in-community housing from two perspectives of housing price and rent.

This study uses the hedonic model to identify the factors affecting the housing value, and the influence of distributed long-rented apartments on the housing value in the community is analyzed from two aspects of housing price and rent by using the ordinary least square method and propensity score matching method.

The primary finding indicates that decentralized long-term rental apartments increase housing prices while decreasing general rental housing rents in the community, with the average degree of increase ranging from 0.93% to 2.59% and the average degree of decrease ranging from 2.23% to 4.34%. According to additional research, the prices of houses within communities rise by 0.042% for every 1% increase in the share of decentralized long-term rentals, while the rents for other types of rental property fall by 0.162%.

The government can regulate the housing market by regulating the access and layout of distributed long-rent apartments.

The findings of this study indicate that the existence and share of distributed long-rent apartments have a heterogeneous impact on the housing price and rent in the community, respectively.