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The paper proposes that globalization has opened up opportunities for developing countries that adopt the right type of political economy doctrine and opt to externalize their economies through contemporarily relevant global integration models and country‐level strategies.

Portraying the various phases of globalization, the experiences of developed and developing countries are reviewed to recommend a “global participation model” for developing countries to benefit from the globalization process.

In profiling the global participation model, the paper introduces the concept of global business chain and shows how countries have formed strategic groups to participate in the global chain as “opportunity exploiters” and “opportunity providers.” If equipped with appropriate and efficient economic doctrine, developing countries could act both as opportunity providers and opportunity exploiters and transform themselves into “twin advantage countries” – with substantial enhancements in their total factor productivity and consequent gains in the quality of life of populations.

Global business chain and twin advantage strategy, the two major propositions of this paper, are a new stream of thought in the discussions on globalization.

This paper proposes an integrated knowledge visualization and digital twin system for supporting strategic management decisions. The concepts and applications of strategic architecture have been illustrated with a concrete real-world case study and decision rules of using the strategic digital twin management decision system (SDMDS) as a more visualized, adaptive and effective model for decision-making.

This paper integrates the concepts of mental and computer models and examines a real case's business operations by applying system dynamics modelling and digital technologies. The enterprise digital twin system with displaying real-world data and simulations for future scenarios demonstrates an improved process of strategic decision-making in the digital age.

The findings reveal that data analytics and the visualized enterprise digital twin system offer better practices for strategic management decisions in the dynamic and constantly changing business world by providing a constant and frequent adjustment on every decision that affects how the business performs over both operational and strategic timescales.

In the digital age and dynamic business environment, the proposed strategic architecture and managerial digital twin system converts the existing conceptual models into an advanced operational model. It can facilitate the development of knowledge visualization and become a more adaptive and effective model for supporting real-time management decision-making by dealing with the complicated dependence of constant flow of data input, output and the feedback loop across business units and boundaries.

Distinguishing between physical and social aggression, this study aimed to examine whether the predictive effect of aggression on resource control is moderated by prosocial behavior and corresponds to a linear or a curvilinear trend. Moderating effects of children's social preference among peers and child sex in this context were also tested.

Based on a sample of 682 kindergarten children (348 girls; average age 72.7 months, 3.6 SD), multilevel regressions revealed additive linear effects of social preference and prosociality on resource control.

Moderate (but not high) levels of social aggression also facilitated resource control for disliked children. There was no such threshold effect for well‐liked children, who increasingly controlled the resource the more socially aggressive they were. In contrast, physical aggression hampered resource control unless used very modestly.

The present study has a number of positive features. First, the distinction between physical and social aggression improves our understanding of the relation between aggression and social competence and sketches a more differentiated picture of the role of different forms of aggression in resource control. Second, this study combines the concept of resource control with the concept of social preference and investigates curvilinear effects of aggression. Third, the direct observation of resource control in the Movie Viewer increases the internal validity of this study.

Rare but high impact (R-HI) disruptions, which are caused by legal changes, socio-technical accidents, or natural disasters, are becoming more frequent and have strong short-term and long-term impacts on performance. Meanwhile, the short-term perspective of managers leads to adoption of mitigation strategies with lower investments and immediate performance improvement. The purpose of this paper is to provide insights on supply chain collaboration (SCC) to establish so-called twin-objective strategy to help both risk mitigation (through moderation effects) and performance improvement (through a direct positive impact). Moreover, power position will be considered as the control variable.

A cross-sectional approach was adopted with primary data collected through a survey in China. Data were analyzed using structural equation modeling with partial least squares estimations. A sub-group model analysis was applied to test the effect of the control variable.

The findings verify that SCC has both a direct positive impact on performance and moderation effects on the relationships between sources of R-HI disruptions and performance. The results of sub-group model analysis illustrate that both powerful and weak focal firms benefit from SCC, but in different ways.

The study shows that the allocation of gains from collaborative advantage should be added to the theory-building of relational view. Meanwhile, the research extends the focal firm’s context to its supply chain’s context so that classic contingency theory can be extended to adequately explain supply chain management phenomena.

The ideas expressed in this work are based on those put into practice at the Okuma Corporation of Japan, one of the world′s leading machine tool manufacturers. In common with many other large organizations, Okuma Corporation has to meet the new challenges posed by globalization, keener domestic and international competition, shorter business cycles and an increasingly volatile environment. Intelligent corporate strategy (ICS), as practised at Okuma, is a unified theory of strategic corporate management based on five levels of win‐win relationships for profit/market share, namely: ,1. Loyalty from customers (value for money) – right focus., 2. Commitment from workers (meeting hierarchy of needs) – right attitude., 3. Co‐operation from suppliers (expanding and reliable business) – right connections., 4. Co‐operation from distributors (expanding and reliable business) – right channels., 5. Respect from competitors (setting standards for business excellence) – right strategies. The aim is to create values for all stakeholders. This holistic people‐oriented approach recognizes that, although the world is increasingly driven by high technology, it continues to be influenced and managed by people (customers, workers, suppliers, distributors, competitors). The philosophical core of ICS is action learning and teamwork based on principle‐centred relationships of sincerity, trust and integrity. In the real world, these are the roots of success in relationships and in the bottom‐line results of business. ICS is, in essence, relationship management for synergy. It is based on the premiss that domestic and international commerce is a positive sum game: in the long run everyone wins. Finally, ICS is a paradigm for manufacturing companies coping with change and uncertainty in their search for profit/market share. Time‐honoured values give definition to corporate character; circumstances change, values remain. Poor business operations generally result from human frailty. ICS is predicated on the belief that the quality of human relationships determines the bottom‐line results. ICS attempts to make manifest and explicit the intangible psychological factors for value‐added partnerships. ICS is a dynamic, living, and heuristic‐learning model. There is intelligence in the corporate strategy because it applies commonsense, wisdom, creative systems thinking and synergy to ensure longevity in its corporate life for sustainable competitive advantage.

The application of digital twins to optimise operations and supply chain management functions is a bourgeoning practice. Scholars have attempted to keep pace with this development initiating a fast-evolving research agenda. The purpose of this paper is to take stock of the emerging research stream identifying trends and capture the value potential of digital twins to the field of operations and supply chain management.

In this work we employ a bibliometric literature review supported by bibliographic coupling and keyword co-occurrence network analysis to examine current trends in the research field regarding the value-added potential of digital twin in operations and supply chain management.

The main findings of this work are the identification of four value clusters and one enabler cluster. Value clusters are comprised of articles that describe how the application of digital twin can enhance supply chain activities at the level of business processes as well as the level of supply chain capabilities. Value clusters of production flow management and product development operate at the business processes level and are maturing communities. The supply chain resilience and risk management value cluster operates at the capability level, it is just emerging, and is positioned at the periphery of the main network.

This is the first study that attempts to conceptualise digital twin as a dynamic capability and employs bibliometric and network analysis on the research stream of digital twin in operations and supply chain management to capture evolutionary trends, literature communities and value-creation dynamics in a digital-twin-enabled supply chain.

The purpose of this paper is to characterise the TWIN-RRT* algorithm which solves a motion planning problem in which an agent has multiple possible targets where none of them is compulsory and retrieves feasible, “low cost”, asymptotically optimal and probabilistically complete paths. The TWIN-RRT* algorithm solves path planning problems for both holonomic and non-holonomic robots with or without kinematic constraints in a 2D environment.

It was designed to work equally well with higher degree of freedom agents in different applications. It provides a practical implementation of feasible and fast planning, namely where a closed loop is required. Initial and final configurations are allowed to be exactly the same.

The TWIN-RRT* algorithm computes an efficient path for a single agent towards multiple targets where none of them is mandatory. It inherits the low computational cost, probabilistic completeness and asymptotical optimality from RRT*.

It uses efficiency as cost function, which can be adjusted to the requirements of any given application. TWIN-RRT also shows compliance with kinematic constraints.

The practical application where this work has been used consists of an autonomous mobile robot that picks up golf balls in a driving range. The multiple targets are the golf balls and the optimum path is a requirement to reduce the time and energy to refill as quickly as possible the balls dispensing machine.

The new random sampling algorithm – TWIN-RRT* – is able to generate feasible efficient paths towards multiple targets retrieving closed-loop paths starting and finishing at the same configuration.

Management strategy within the Ford Motor Company has been going through a quiet revolution in the last decade. From being a utility car producer understanding competitive advantage exclusively in cost terms and equating management with control Ford have adopted a long‐term strategy of design‐led product innovation and promoting high trust labour relations. At the heart of this change strategy are the twin thrusts of ‘Participative Management’ aimed primarily at the executive level and ‘Employee Involvement’ for salaried and hourly employees. Both projects were borne of the watershed, if inherently flawed, ‘After Japan’ initiative of the early 1980s. ‘After Japan’ represented Ford's assimilation of the organisational — as well as economic — challenge thrown down by Japanese manufacturing. But what began as an ambitious attempt to impose Japanese factory institutions such as quality circles on a suspicious workforce rapidly degenerated into a conventional cost‐cutting exercise. From this experience, however, Ford's strategists moved towards a more processual, less mechanistic, understanding of organisational change. We shall examine the results of the ‘El’ process in a variety of settings and the impact of ‘PM’ on decision‐making processes within Ford UK.

Twin support vector machine (TSVM) is an effective machine learning technique. However, the TSVM model does not consider the influence of different data samples on the optimal hyperplane, which results in its sensitivity to noise. To solve this problem, this study proposes a twin support vector machine model based on fuzzy systems (FSTSVM).

This study designs an effective fuzzy membership assignment strategy based on fuzzy systems. It describes the relationship between the three inputs and the fuzzy membership of the sample by defining fuzzy inference rules and then exports the fuzzy membership of the sample. Combining this strategy with TSVM, the FSTSVM is proposed. Moreover, to speed up the model training, this study employs a coordinate descent strategy with shrinking by active set. To evaluate the performance of FSTSVM, this study conducts experiments designed on artificial data sets and UCI data sets.

The experimental results affirm the effectiveness of FSTSVM in addressing binary classification problems with noise, demonstrating its superior robustness and generalization performance compared to existing learning models. This can be attributed to the proposed fuzzy membership assignment strategy based on fuzzy systems, which effectively mitigates the adverse effects of noise.

This study designs a fuzzy membership assignment strategy based on fuzzy systems that effectively reduces the negative impact caused by noise and then proposes the noise-robust FSTSVM model. Moreover, the model employs a coordinate descent strategy with shrinking by active set to accelerate the training speed of the model.

The recent outbreak of the coronavirus disease 2019 (COVID-19) pandemic has severely disrupted the operations of global supply chains (SCs) providing an opportunity for decision-makers to rethink and tune their existing strategies. To tackle the impacts caused by such a pandemic, this study aims to examine the contextual relations among the factors influencing supply chain viability (SCV) for achieving long-term Sustainable Development Goals (SDGs).

A decision-aid approach by integrating Pareto analysis, grey theory and total interpretive structural modeling (TISM) has been propounded. The proposed approach examines contextual relations among the factors for SCV for small and medium-sized enterprises (SMEs) in an emerging economy. This study contributes to theory, methodology and practice by exploring a new research problem in the context of the SCV and COVID-19 pandemic and by developing a new decision-aid approach.

The findings reveal that the creation of SC digital twin and transformation of SCs to supply networks would help the policymakers to deal with the “new normal.” Also, SC crowdfunding and policy development for health protocols are critical driving factors influencing SCV.

This research work is perhaps one of few initial attempts to advance the theoretical and practical understanding of SCV and to achieve SDGs in industries following a pandemic risk.