Purpose The selection of alliance partners occurs in a highly uncertain environment, characterized by the uncertainty related to the parties themselves and ambiguities in evaluation criteria and weights. Moreover, it is crucial to fully consider the psychological expectations of the parties involved in the partner selection process. The authors focus on the problem of partner selection, considering the psychological expectations of decision makers (DMs) under uncertain information. Design/methodology/approach Interval grey numbers (IGNs) provide an effective solution to this problem. Firstly, the authors incorporate the psychological expectations of parties into the decision-making framework and utilize IGNs to represent the evaluation and expectation information of universities and enterprises. Then, the authors employ cumulative prospect theory (CPT) to construct prospect value matrices for universities and enterprises and solve the partner selection two-sided matching (TSM) model with the objectives of minimizing losses and maximizing the number of matching pairs. Findings Through comparison, it is evident that the proposed method effectively incorporates the psychological expectations of DMs, addresses the challenges of cooperative matching under uncertain information and improves both the satisfaction and stability of the matching outcomes. By integrating the psychological expectations of the parties involved, the proposed matching scheme achieves a satisfaction level of 0.99, with comparatively high robustness. Originality/value In this paper, the authors propose a novel method for alliance partner selection under uncertainty, based on psychological expectations. IGNs are used to represent evaluation and expectation information of DMs, aligning with real-world ambiguity. CPT is then applied to calculate gains and losses based on loss aversion and to determine criteria weights for maximizing prospect values. Furthermore, a TSM method is proposed to enhance satisfaction and stability. Additionally, the proposed method is validated through a real-case scenario and compared with another method.

PurposeThis paper aims to optimize supply chain information decision-making systems to better manage complex, high-dimensional and uncertain information through the integration of fuzzy logic and neural network technology.Design/methodology/approachA framework based on fuzzy logic reasoning is developed to address empirical issues in traditional supply chain systems. Subsequently, an innovative radial basis function-dynamic fuzzy neural network (RBF-DFNN) model is constructed, enhancing the system’s capability to interpret uncertain information. This model retains the advantages of traditional dynamic fuzzy neural networks (DFNN) while introducing an anti-fuzzy layer and optimizing the membership function and T-paradigm layers.FindingsThe RBF-DFNN model leads to the creation of a high-dimensional information decision-making model for supply chains. Experimental results indicate that this model effectively utilizes the K-medoids clustering algorithm to accurately capture the high-dimensional characteristics and intrinsic correlations of supply chain data. Parameter optimization significantly improves the model’s performance, with the root mean squared error (RMSE) and mean absolute error (MAE) enhanced, resulting in coefficients of determination rising from 95.6 and 97.8–99.1% compared to STPF-AIMM and ANFIS networks.Originality/valueThis study contributes to the advancement of supply chain management by developing a highly intelligent and refined decision-making model, enhancing the intelligence level of intelligent storage systems and promoting more sophisticated supply chain operations.

Purpose This study examines the factors influencing users' intentions to adopt virtual reality (VR) technologies in museums, emphasizing the role of social cognitive theory (SCT) constructs and trust dimensions (ability, integrity and benevolence). Design/methodology/approach A survey of 413 university students from the UAE, KSA and Kuwait was conducted, and partial least squares structural equation modeling (PLS-SEM) was applied to analyze the relationships between SCT constructs, trust and behavioral intention. Findings SCT constructs significantly impact trust and intention to use VR in museums. Trust is a key mediator whose dimensions play pivotal roles in shaping behavioral intentions. Gender moderates these relationships, while cultural and age differences have minimal effects. Originality/value This study advances understanding of the psychological and trust-based factors driving VR adoption in cultural contexts. It provides actionable insights for enhancing users’ adoption of Metaverse-based museums, supporting practitioners and policymakers in leveraging VR technologies within the Metaverse.

PurposeThe purpose of this study is to explore the relationship between entrepreneurial training (ET), entrepreneurial intentions (EIs) and intrapreneurial behaviour (IB), with a focus on how cognitive dissonance rises due to opportunity constraints. By integrating cognitive dissonance theory with entrepreneurial and intrapreneurial frameworks, the research aims to provide a deeper understanding of the psychological and environmental factors that influence the effectiveness of ET.Design/methodology/approachThis study employs a conceptual framework based on a literature review and theoretical analysis. It synthesises existing research on ET, cognitive dissonance and opportunity constraints to develop propositions. The study combines qualitative insights with theoretical modelling to explore the interplay between training, intentions and behaviour.FindingsThe study claims that ET enhances EIs and IB but can lead to cognitive dissonance when opportunities are limited. Entrepreneurial opportunity constraints are proposed to significantly moderate the effectiveness of training.Practical implicationsThe study suggests that ET programmes should address potential cognitive dissonance and include strategies for creating supportive environments that improve entrepreneurial and intrapreneurial outcomes. To enhance innovation, policymakers should foster supportive ecosystems and organisations should align resources with intrapreneurial goals. These insights can help optimise training effectiveness and support entrepreneurial success.Originality/valueThis study uniquely integrates cognitive dissonance theory with ET frameworks, offering novel insights into how opportunity constraints affect entrepreneurial outcomes. It provides a fresh perspective on the psychological and environmental factors influencing training effectiveness, contributing valuable theoretical and practical guidance for enhancing entrepreneurship and intrapreneurship.

PurposeThis study systematically aims to investigate the interplay between store brand (SB) introduction and demand information sharing strategies under uncertain market conditions. By analyzing these two critical factors together, we aim to address their impact on the equilibrium decisions and profits of supply chain members.Design/methodology/approachUsing a game-theoretic approach, we develop a multi-stage model to analyze the retailer’s decision-making process across four distinct scenarios: (1) absence of SB introduction and no information sharing, (2) absence of SB introduction with information sharing, (3) presence of SB introduction and no information sharing and (4) presence of SB introduction with information sharing. The model captures the stochastic nature of market demand, incorporates cost coefficients and accounts for quality differentiation between store brands and national brands.FindingsThe findings reveal that the coexistence of national and store brands is viable only when the production cost coefficient falls within a moderate range. A low-cost coefficient may force the manufacturer to exit due to the “quality squeeze-out” effect, wherein the SB’s superior value proposition undermines the NB’s competitiveness. Conversely, a high-cost coefficient may result in manufacturer exit owing to the “cost squeeze-out” effect, where production costs render the NB unprofitable. Introducing an SB enhances the retailer’s profit but diminishes the manufacturer’s, although the retailer’s gain outweighs the manufacturer’s loss, leading to an overall increase in supply chain profitability. Without SB introduction, demand information sharing reduces both the retailer’s and the supply chain’s profits while increasing the manufacturer’s profit. However, when an SB is introduced and the cost coefficient is low, sharing demand information further boosts the retailer’s profit.Originality/valueThis study offers a unique contribution by examining the interaction between SB introduction and demand information sharing under uncertain market conditions. Unlike previous research, which often considers these factors in isolation, our work explores their mutual influence and impacts on supply chain outcomes. We provide a novel perspective on how cost coefficients shape strategic decisions, identifying conditions under which SB introduction and information sharing benefit retailers or disadvantage manufacturers. Furthermore, our model delivers practical insights into supply chain profitability by addressing the dual effects of quality and cost squeeze-out.

PurposeThe purpose of this study is to investigate resource-constrained multi-project scheduling problems (RCMPSP) involving uncertainty in the form of time-dependent renewable resource reliability. A key focus is to minimize the makespan (completion time) of projects when resources can become unavailable or fail over time at non-constant rates. Accounting for realistic resource reliability seeks to provide scheduling solutions that better reflect potential delays in practical multi-project environments.Design/methodology/approachA new discrete-time binary integer programming formulation of RCMPSP is expanded to include time-dependent resource reliability and simultaneously evaluate the time-dependent failure rate and constant repair rate of a resource. A new hybrid immune genetic algorithm with local search (HIGALS) is developed to solve this NP-hard problem. HIGALS incorporates a new coding mechanism, initialization method and local search operator.FindingsA case study tests the proposed HIGALS approach. The validity of the mathematical model is confirmed by solving small-sized problems with GAMS software. The proposed HIGALS algorithm is validated by solving small-sized problems and comparing its solutions with GAMS. The superiority of HIGALS is demonstrated by comparing its solutions with six basic algorithms on medium- and large-sized problems. Results show that HIGALS outperforms existing algorithms, achieving an average reduction in makespan of over 11.79%, while maintaining the advantages of genetic, immune and local search algorithms and avoiding their disadvantages.Practical implicationsConsidering time-dependent resource reliability can help project managers plan for disruptions and delays in resource-critical projects. HIGALS provides decision support for robust multi-project scheduling.Originality/valueThis study contributes to the field by investigating RCMPSP with time-dependent renewable resource reliability, which reflects real-world uncertainty more accurately. HIGALS presents a novel approach to balance intensification and diversification for this challenging problem.

Purpose Paradigm change in systems thinking is justified, and a new direction is suggested. The change of state of equilibrium from unsatisfactory to acceptable driven by a dynamic system is described as problem-solving which is innate and universal in the living sphere. An empirical theory of problem-solving is proposed which integrates by design or chance the mental effort of creating the envisaged change with the symbolic structure of “product – dynamic system” which drives the former. Design/methodology/approach The elementary constituent of structures and systems is identified as 1 – and 2 – place sentences or ordered pairs, the smallest parts of natural language which make sense and of which complex, linguistic structures can be constructed by following the stages of “linguistic modelling”. This leads to sequences of predicate logic conditionals representing the propagation of state or the reasoning structure which may be related to AI. A “story” of a problematic scenario in natural language is transformed into its analytical equivalent. Findings Problem-solving approaches have a long historical background ending with the current, extensive “systems thinking” but none has come up with a comprehensive, empirical approach the reasons for which is discussed. The proposed theory clarifies the role of conventional science, the arts and engineering in problem-solving. It is rooted in current branches of knowledge, involves the use of mathematics and other disciplines and is highly teachable, needs debate, software and further developments. Originality/value Mathematical modelling is restricted to the use of quantitative properties. The symbolic structure of the proposed theory is processed, natural language which admits the use of emotive properties as part of logical conditionals derived from semantic diagrams. Systems thinking is now part of problem-solving. The analytical content allows the inclusion into teaching curriculum at school and higher education level, at the same time the method can be adopted to informal applications. An integrated theory of creative and methodical problem-solving driven by matching systems/product by design as well as static systems as part of dynamic systems is presented. Problem-solving is innate and universal in living things; it is the primary phenomenon to be identified driving its own means of resolution: the system.

Purpose This study aims to examine the spatial spillover effect and its mechanism of the impact of China–US relations on Chinese enterprises’ OFDI. Design/methodology/approach This study takes the relations between China and the United States (China–US relations), one of the most important great power relations in the 21st century, as the research object, employing a spatial spillover perspective informed by bilateral and multilateral theory, information transfer theory and realism and institutionalism. This study utilizes micro-level data on Chinese firms’ outward foreign direct investment (OFDI) for 2008–2023 to study the spatial spillover effects and mechanism involved in the impact of China–US relations on Chinese firms’ OFDI. Findings Results show that China–US relations increase Chinese enterprises’ OFDI in countries other than the US, demonstrating spatial spillovers in the impact of China–US relations on Chinese firms’ OFDI. This spatial spillover effect is characterized by a full cycle, time variation and imbalance. The mechanism analysis shows that this spatial spillover is realized through the neighboring country effects and great power demonstration effects. Heterogeneity analysis shows that firms’ independent research and development (R&D) capabilities, China’s economic power and the cultural distance between China and the host country can buffer the impact of changes in China–US relations on Chinese firms’ OFDI. Practical implications This study provides a comprehensive understanding of the role of international relations in influencing OFDI and offers useful insights for developing countries to promote high-level opening-up. Originality/value First, this study examines the spatial spillover effects of China–US relations on Chinese firms’ OFDI, expanding the perspective of the impact of diplomatic relations on cross-border economic activities. Second, this study enriches the research on the mechanism by which diplomatic relations affect OFDI by arguing that this spatial spillover is realized through the neighboring country effect and the great power demonstration effect. Third, this study confirms the role of enhancing the competitiveness of multinational corporations (MNCs), upgrading the country’s comprehensive national strength, and promoting cultural exchanges in buffering exogenous shocks of changes in China–US relations.

Purpose This study investigates the evolution of digital transformation research trends in the context of the fashion industry. The paper analyzes and addresses the impact of digital innovations on three areas of the industry: products, processes and business models.Design/methodology/approach The study examined 277 peer-reviewed articles using various bibliometric tools and indicators, aiming to identify and organize the contributions of the most influential works. Additionally, a critical review of the ten most cited papers in the field was carried out.Findings The study shows that digitalization is not merely a technological trend; rather, it is a transformative force reshaping the fashion industry. It fosters collaboration, innovation and sustainability, creating an ecosystem where businesses can thrive by aligning with circular principles and leveraging digital tools.Research limitations/implications The paper reveals a gap in the scientific systematization and exploration of the variety of applications of digital tools in the fashion industry. The study’s limitations include the keywords chosen for the research and the period of the research from 1998 to 2023.Originality/value The paper pursues to contribute to the current discussion on sustainable practices within the fashion industry, while also setting the foundation for future progress in digital innovation that supports the increasing need for sustainable and personalized products.

Purpose The rapid development of generative artificial intelligence has witnessed its widespread integration across various industries, contributing to enhanced productivity. However, a comprehensive exploration of the underlying factors influencing the behavior of graphic designers in employing such tools remains incomplete. This research aims to amalgamate the IDT theory with the UTAUT2 model to construct a structural model, delving into the factors affecting graphic designers’ behavior in using GenAI tools. Design/methodology/approach A survey was conducted with 394 respondents, and the results were analyzed using PLS-SEM. Findings The findings reveal that most factors proposed in both the UTAUT2 and IDT theories exert positive influences. Notably, the study highlights that AI anxiety significantly influences designers’ usage behavior. Originality/value This research provides a theoretical foundation and practical guidance for both graphic designers and AI developers.