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The purpose of this paper is to analyse event management by using three value creation logics, value chain, value network and value shop. In event management, value is generated through intermediation where the dominant creation logic is a value network. However, the complexity of events and danger of unexpected problems is increasing, which, in the worst case, leads to event failure. This fact makes it necessary to change the general attitude towards this topic from risk management to uncertainty management and use the value shop in order to solve problems efficiently.

This paper is based on the methodology of phenomenological hermeneutics which analyzes the object of study by interpreting the facticity and provides basics to generate a conceptual model.

The dominant value creation logic must be changed to prevent the value network from failure in generating value, since only the value shop provides high quality problem solving. Trust not only in planning but also in the own problem-solving competence and available tools is a major part of the value shop. As a practical example of high quality problem solving, the performance of high reliability organisations can be used by event managers.

Using these hermeneutical gained logic, additional empirical research projects in event management, leadership and problem-solving competence of top managers, are promptly intended. Additionally, studies concerning competences and structures of the uncertainty management team have to be determined and developed as well as education and coaching has to be generated in order to achieve best results in problem solving.

Practical implications of this paper are: considering the value shop as the dominant value creation logic in uncertainty management; establishing a specially trained Complex Problem-Solving Team; and considering trust to be an essential element of the value shop.

The basic job requirements a successful value net (event-) manager has to provide in such a complex system are: acting as integrator, mediator and problem solver simultaneously. Additionally event managers need to be trained to rethink the value creation logic and use the value shop within the value net to stay flexible and work successfully during their events.

Derived from this new perspective the necessity of enhancing the implemented value creation logic according to uncertainties allows event managers to solve unexpected problems faster and more efficiently.

This study aims to explore the role of planned, sudden shifts in lived experiences, in influencing learner capabilities towards improved problem-solving for sustainable development outcomes. The authors responded to employers of engineering and built environment graduates observing limited “real-life” problem-solving skills, beyond using established formulae and methods, in spite of attempts over more than two decades, to train engineers and other built environment disciplines in areas such as whole system design and sustainable design.

A grounded theory approach was used to guide the analysis of data collected through ethnographic methods. The process involved reflecting on authors’ efforts to develop context appreciation within a course called “International Engineering Practice”, using two years of collected data (archived course information, including course profile; completed assessment; lecture and field visit evaluations; and focus groups). The study is built on the authors’ working knowledge of Bloom’s Taxonomy and Threshold Learning Theory, and the well-established role of “context appreciation” in complex problem-solving. After the first iteration of the course, the authors looked for additional theoretical support to help explain findings. The Cynefin framework was subsequently used to augment the authors’ appreciation of “context” – beyond physical context to include relational context, and to evaluate students’ competency development across the four domains of “clear”, “complicated”, “complex” and “chaotic”.

This study helped the authors to understand that there was increased capacity of the students to distinguish between three important contexts for problem-solving, including an increased awareness about the importance of factual and relevant information, increased acknowledgement of the varying roles of professional practitioners in problem-solving depending on the type of problem and increased appreciation of the importance of interdisciplinary teams in tackling complex and complicated problems. There were several opportunities for such courses to be more effective in preparing students for dealing with “chaotic” situations that are prevalent in addressing the United Nations’ 17 sustainable development goals (UNSDGs). Drawing on the course-based learnings, the authors present a “context integration model” for developing problem-solving knowledge and skills.

The research findings are important because context appreciation – including both physical context and relational context – is critical to problem-solving for the UNSDGs, including its 169 targets and 232 indicators. The research findings highlight the opportunity for the Cynefin framework to inform holistic curriculum renewal processes, enhancing an educator’s ability to design, implement and evaluate coursework that develops physical and relational context appreciation.

The study’s findings and context integration model can help educators develop the full range of necessary problem-solving graduate competencies, including for chaotic situations involving high degrees of uncertainty. Looking ahead, acknowledging the significant carbon footprint of global travel, the authors are interested in applying the model to a domestic and/or online format of the same course, to attempt similar learning outcomes.

Connecting Bloom’s taxonomy deep learning and threshold learning theory critical path learning insights with the Cynefin framework context domains, provides a novel model to evaluate competency development for problem-solving towards improved holistic physical and relational “context appreciation” outcomes.

Organizations, especially those adapting to rapidly changing environments, face the challenge of being able to solve complex problems within highly constrained timeframes. Complex problem solving has been addressed by theories of bounded rationality. However, these theories focus on solving complex but structured problems, and thus, context and how it emerges and transforms is not a central issue. More recently, theories of the firm as a knowledge‐creating entity have focused on how organizations solve complex unstructured problems. These theories suggest that context and contextualization are central elements in problem solving. Yet, no understanding of how context emerges and transforms emerges from these theories. The present paper focuses on the emergence and transformation of context in solving complex unstructured problems, attempts to remedy the shortcomings of the theories described above and investigates the nature of context. Concludes by explaining its role in tacit knowledge sharing.

This paper aims to evaluate the effect of knowledge overlap, search width and problem complexity on the quality of problem-solving in teams that use the majority rule to aggregate heterogeneous knowledge of the team members.

The paper uses agent-based simulations to model iterative problem-solving by teams. The simulation results are analyzed using linear regressions to show the interactions among the variables in the model.

We find that knowledge overlap, search width and problem complexity interact to jointly impact the optimal solution in the iterative problem-solving process of teams using majority rule decisions. Interestingly, we find that more complex problems require less knowledge overlap. Search width and knowledge overlap act as substitutes, weakening each other’s performance effects.

The results suggest that team performance in iterative problem-solving depends on interactions among knowledge overlap, search width and problem complexity which need to be jointly examined to reflect realistic team dynamics.

The findings suggest that team formation and the choice of a search strategy should be aligned with problem complexity.

This paper contributes to the literature on problem-solving in teams. It is the first attempt to use agent-based simulations to model complex problem-solving in teams. The results have both theoretical and practical significance.

The main focus of this paper is to discuss appropriate forms of higher education for building up students' competence for working on complex real‐world problems.

Within this paper the Harvard approach is accurately compared with the ETH approach by discussing theoretical and practical implications as well.

It is argued that the Harvard case study approach is a sensible approach to bridging the gap between the academic and the practical world, but it has important limits in preparing students to cope with complex real‐world problems. In some important respects, the ETH case study approach goes further by exposing students directly to the multi‐faceted and complex character of real‐world problems.

The ETH approach puts additional demands on students and teachers to bridge the gap between university and society with a high degree of responsibility. Consequently, a combination of both the Harvard and the ETH approach might be interesting.

The comparison of the Harvard case study approach with the ETH case study approach is novel. The discussion of educational together with practical implications provides insight to the peculiarities of each single approach together with an orientation for their implementation within higher education. Guidance is given to universities who are deciding what educational means have to be implemented in order to prepare their students for the task of solving complex real‐world problems in an inter but also transdisciplinary manner.

Organizations are faced with solving increasingly complex problems. Addressing these issues requires effective leadership that can facilitate a collaborative problem solving approach where multiple perspectives are leveraged. In this conceptual paper, we critique the effectiveness of earlier leadership models in tackling complex organizational issues. We then examine one promising model, adaptive leadership, in detail and propose that this model provides a leadership approach for addressing current organizational realities. The model, proposed and developed over the last two decades, fundamentally supports the assumption of leadership by multiple stakeholders, with the formulation of the leadership dependent on the emergent problem. Adaptive leadership, with its focus on collaborative problem-solving utilizing multiple perspectives, is especially applicable to large organizations faced with solving complex problems involving many stakeholders.

This study aims to explore the conditional relationships between supplier involvement and financial performance. From a problem-solving perspective, this study takes the focal firm as a problem-solving system. It is postulated that supplier involvement enhances financial performance by helping solve complex problems of new product development. Furthermore, product modularity and smartness are considered contingent factors to clarify the boundary conditions.

The ordinary least squares regression was conducted to test the hypotheses based on survey data from 136 high-tech firms in China.

Supplier involvement is positively related to financial performance. Product modularity weakens the impact of supplier involvement on financial performance. Furthermore, product smartness strengthens the negative influence of product modularity on the relationship between supplier involvement and financial performance.

This study combines supplier involvement with product attributes. It takes a problem-solving perspective to rethink suppliers’ roles in new product development as problem-solvers rather than resource holders. Furthermore, this study advances the encapsulation effects of product modularity and smartness to influence the supplier involvement–financial performance link.

This study aims at determining the factors that favor a systematic approach to deal with complex operational and strategic problems. Management literature on problem-solving makes a clear distinction between either fixing a problem temporarily by eliminating its symptoms or solving it by diagnosing and altering underlying causes. Adopting a cognitive perspective of the dual-processing theory, this study labels these two approaches intuitive problem-solving and systematic problem-solving (SPS). While the superior effectiveness of SPS in fostering organizational learning is widely documented, existing literature fails to provide an overview of the conditions that support the adoption of SPS.

This paper presents a systematic literature review to shed light on the main supporting factors of SPS in operational as well as strategic domains.

Seven supporting factors of SPS (namely, nature of the problem, time availability, information availability, collaborative culture, transformational leadership, organizational learning infrastructure and environmental dynamism) are first identified and then discussed in an integrative model.

This work is an original attempt to inclusively address organizational, environmental and problem nature-related factors that favor SPS adoption. By determining the SPS supporting factors, this study highlights why many organizations fail or struggle to implement and sustain SPS over time.

To help workers make the right decision, over the years, technological solutions and workplace learning analytics systems have been designed to aid this process (Ruiz-Calleja et al., 2019). Recent developments in artificial intelligence (AI) have the potential to further revolutionise the integration of human and artificial learning and will impact human and machine collaboration during team work (Seeber et al., 2020).

Complex problem-solving has been identified as one of the key skills for the future workforce (Hager and Beckett, 2019). Problems faced by today's workforce emerge in situ and everyday workplace learning is seen as an effective way to develop the skills and experience workers need to embrace these problems (Campbell, 2005; Jonassen et al., 2006).

In this commentary the authors argue that the increased digitization of work and social interaction, combined with recent research on workplace learning analytics and AI opens up the possibility for designing automated real-time feedback systems capable of just-in-time, just-in-place support during complex problem-solving at work. As such, these systems can support augmented learning and professional development in situ.

The commentary reflects on the benefits of automated real-time feedback systems and argues for the need of shared research agenda to cohere research in the direction of AI-enabled workplace analytics and real-time feedback to support learning and development in the workplace.

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.