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We revisit the problem of redesigning the Master in Business Administration (MBA) program, curriculum, and pedagogy, focusing on understanding and seeking to tame its “wicked problems,” as an intrinsic part and challenge of the MBA program venture, and to render it more realistic and relevant to address major problems and their consequences. We briefly review the theory of wicked problems and methods of dealing with their consequences from multiple perspectives. Most characterization of problems classifies them as simple (problems that have known formulations and solutions), complex (where formulations are known but not their resolutions), unstructured problems (where formulations are unknown, but solutions are estimated), and “wicked” (where both problem formulations and their resolutions are unknown but eventually partially tamable). Uncertainty, unpredictability, randomness, and ambiguity increase from simple to complex to unstructured to wicked problems. A redesigned MBA program should therefore address them effectively through the four semesters in two years. Most of these problems are real and affect life and economies, and hence, business schools cannot but incorporate them into their critical, ethical, and moral thinking.

In this chapter, the author delves deeper into adaptive and sustainability challenges, specifically discussing the roles of management and leadership in addressing these problems. Both responsible leadership and management are essential for tackling sustainability challenges. Leadership focuses on establishing a vision and inspiring others to attain it, while management is concerned with devising and executing strategies to realize that vision. Addressing wicked problems necessitates collaboration, engagement and innovative solutions, involving both leadership and management. To effectively conquer sustainability challenges, organizations must embrace a more holistic and sustainable approach to management and leadership. This might include collaborating with stakeholders to discover innovative solutions that prioritize sustainability and social responsibility. Resolving wicked problems calls for a distinct management and leadership approach that is cooperative, systemic, and sustainable.

Common literature review methods such as systematic review and narrative review are poorly suited to the investigation of complex management phenomena. Systematic reviews are highly driven by protocol and procedure, and are oft-criticized as reductive and poorly equipped to examine the interaction between phenomena and context, nonlinear processes, and empirical outcomes that are less predictable. Narrative reviews, on the other hand, are pluralistic and iterative and thus better suited to descriptions of the complex and unpredictable; however, they tend to lack methodological transparency, trustworthiness, and pragmatism in application. The “realist synthesis” approach to literature review can be seen as the middle-ground between these two common methods, offering both methodological rigor alongside flexibility and nuance. Realist synthesis takes an explanatory frame, with a focus on unearthing the theorized causal mechanisms at play beneath a phenomenon of interest.

Quality management (QM) can support organisations in contributing to sustainable development. As a result of an expanding focus from customers towards stakeholders within QM, the perspectives to consider multiply. Understanding how practices and tools for process management are specifically affected by this increase in perspectives is key to creating the right conditions for improvement initiatives that support sustainable development.

This paper constructs a typology wherein the use of process management practices and tools is described in nine distinguished system contexts. Inductive discrimination is used to differentiate the system contexts and different use cases for process practices and tools.

Using the system of systems grid (SOSG), mainstream business process management (BPM) practices are positioned in a simple unitary context, whilst sustainability challenges also involve more complex contexts. Addressing these challenges requires integrating new tools and methods from paradigms outside of traditional functionalist business process management practices.

This paper highlights the necessity to consider system contexts when developing feasible practices and tools for effective process management.

Practical implications are that quality practitioners aiming to exploit the potential in process management to support sustainability get support for planning and conducting process improvement initiatives aiming to consider several stakeholder perspectives.

This paper presents a new typology for understanding the context of QM process initiatives and BPM in light of a contemporary sustainability focus.

For successful management of construction projects, a precise analysis of the balance between time and cost is imperative to attain the most effective results. The aim of this study is to present an innovative approach tailored to tackle the challenges posed by time-cost trade-off (TCTO) problems. This objective is achieved through the integration of the multi-verse optimizer (MVO) with opposition-based learning (OBL), thereby introducing a groundbreaking methodology in the field.

The paper aims to develop a new hybrid meta-heuristic algorithm. This is achieved by integrating the MVO with OBL, thereby forming the iMVO algorithm. The integration enhances the optimization capabilities of the algorithm, notably in terms of exploration and exploitation. Consequently, this results in expedited convergence and yields more accurate solutions. The efficacy of the iMVO algorithm will be evaluated through its application to four different TCTO problems. These problems vary in scale – small, medium and large – and include real-life case studies that possess complex relationships.

The efficacy of the proposed methodology is evaluated by examining TCTO problems, encompassing 18, 29, 69 and 290 activities, respectively. Results indicate that the iMVO provides competitive solutions for TCTO problems in construction projects. It is observed that the algorithm surpasses previous algorithms in terms of both mean deviation percentage (MD) and average running time (ART).

This research represents a significant advancement in the field of meta-heuristic algorithms, particularly in their application to managing TCTO in construction projects. It is noteworthy for being among the few studies that integrate the MVO with OBL for the management of TCTO in construction projects characterized by complex relationships.

In this chapter, the discussion revolves around the necessity for responsible management within the context of a complex environment. The 21st century has brought forth numerous challenges for businesses and organizations globally. The rise of VUCA has made it increasingly difficult for companies to achieve success using traditional management approaches. Addressing adaptive challenges and wicked problems, which are sustainability issues that are hard to define and even harder to solve, has become a significant concern for today's enterprises. To tackle these challenges, a shift in management and leadership paradigms is needed, particularly a shift from ego-centric to eco-centric management styles and from boss-centric to self-management approaches. Embracing a more responsible and sustainable management strategy is essential for addressing adaptive challenges. This could involve integrating Taoist principles like compassion, conservation and compliance into management practices and collaborating with stakeholders to devise innovative solutions to tackle adaptive challenges and wicked problems.

The purpose of this paper is to propose a novel improved teaching and learning-based algorithm (TLBO) to enhance its convergence ability and solution accuracy, making it more suitable for solving large-scale optimization issues.

Utilizing multiple cooperation mechanisms in teaching and learning processes, an improved TBLO named CTLBO (collectivism teaching-learning-based optimization) is developed. This algorithm introduces a new preparation phase before the teaching and learning phases and applies multiple teacher–learner cooperation strategies in teaching and learning processes. Applying modularization idea, based on the configuration structure of operators of CTLBO, six variants of CTLBO are constructed. For identifying the best configuration, 30 general benchmark functions are tested. Then, three experiments using CEC2020 (2020 IEEE Conference on Evolutionary Computation)-constrained optimization problems are conducted to compare CTLBO with other algorithms. At last, a large-scale industrial engineering problem is taken as the application case.

Experiment with 30 general unconstrained benchmark functions indicates that CTLBO-c is the best configuration of all variants of CTLBO. Three experiments using CEC2020-constrained optimization problems show that CTLBO is one powerful algorithm for solving large-scale constrained optimization problems. The application case of industrial engineering problem shows that CTLBO and its variant CTLBO-c can effectively solve the large-scale real problem, while the accuracies of TLBO and other meta-heuristic algorithm are far lower than CLTBO and CTLBO-c, revealing that CTLBO and its variants can far outperform other algorithms. CTLBO is an excellent algorithm for solving large-scale complex optimization issues.

The innovation of this paper lies in the improvement strategies in changing the original TLBO with two-phase teaching–learning mechanism to a new algorithm CTLBO with three-phase multiple cooperation teaching–learning mechanism, self-learning mechanism in teaching and group teaching mechanism. CTLBO has important application value in solving large-scale optimization problems.

The purpose of this paper is twofold: to identify and map contemporary research on advanced technology implementations for problem-solving purposes in the manufacturing industry, and to further understand the organizational learning possibilities of advanced technology problem-solving in the manufacturing industry.

This paper outlines a scoping review of contemporary research on the subject. The findings of the review are discussed in the light of theories of contradicting learning logics.

This paper shows that contemporary research on the subject is characterized by technological determinism and strong solution-focus. A discussion on the manufacturing industries’ contextual reasons for this in relation to contradicting learning logics shows that a Mode-2 problem-solving approach could facilitate further learning and expand knowledge on advanced technology problem-solving in the manufacturing industry. A research agenda with six propositions is provided.

The introduction of advanced technology implies complex effects on the manufacturing industry in general, while previous research shows a clear focus on technological aspects of this transformation. This paper provides value by providing novel knowledge on the relationship between advanced technology, problem-solving and organizational learning in the manufacturing industry.

This empirical study uncovers emotional sensemaking factors that cause changes in management perceptions about wicked strategic problems under dynamic complexity. These perception changes improve understanding of, and solutions to, the wicked problem.

Senior managers from three large organizations in different sectors participated in gaming simulation workshops. The strategic issues at stake were intractable and divisive. Qualitative methods captured participants' perceptions of the problems and the dynamic complexity that they faced and how they changed.

Flawed management perceptions were revised as sensemaking processes were catalyzed by emotions of shock/surprise that came from experiencing unexpected stakeholder conduct within a simulation. The plausibility of the conduct was strengthened because managers were role-playing stakeholders. The shock/surprise emotion uncoupled attachment to entrenched beliefs, leading to a willingness to revise the flawed perceptions. The changed perceptions created new insights for a solution to the wicked problem.

Practical implications are how management practitioners can improve the tackling of wicked strategic problems through the use of shock and surprise in a gaming simulation.

This research extends theory on the role of emotions in sensemaking under dynamic complexity. The authors uncover how a hierarchy of managers' emotions used in sensemaking explains the catalytic effect of the shock and surprise of unexpected stakeholder conduct on revisions to their perceptions of the outcomes of the dynamic complexity.

Path planning is an important part of UAV mission planning. The main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization (PSO) such as easy to fall into the local optimum, so that the improved PSO applied to the UAV path planning can enable the UAV to plan a better quality path.

Firstly, the adaptation function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself. Secondly, the standard PSO is improved, and the improved particle swarm optimization with multi-strategy fusion (MFIPSO) is proposed. The method introduces class sigmoid inertia weight, adaptively adjusts the learning factors and at the same time incorporates K-means clustering ideas and introduces the Cauchy perturbation factor. Finally, MFIPSO is applied to UAV path planning.

Simulation experiments are conducted in simple and complex scenarios, respectively, and the quality of the path is measured by the fitness value and straight line rate, and the experimental results show that MFIPSO enables the UAV to plan a path with better quality.

Aiming at the standard PSO is prone to problems such as premature convergence, MFIPSO is proposed, which introduces class sigmoid inertia weight and adaptively adjusts the learning factor, balancing the global search ability and local convergence ability of the algorithm. The idea of K-means clustering algorithm is also incorporated to reduce the complexity of the algorithm while maintaining the diversity of particle swarm. In addition, the Cauchy perturbation is used to avoid the algorithm from falling into local optimum. Finally, the adaptability function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself, which improves the accuracy of the evaluation model.