Search results

We extend the classical garbage can model to examine how individual differences in ability and motivation will influence organizational performance. We find that spontaneous coordination provided by an organized anarchy is superior when agents are equally competent. The Weberian bureaucracy of planned coordination is effective when problems require specialist knowledge. However, errors in matching problems to specialized agents are a central challenge for bureaucracies. Actual organizations, therefore, combine elements of organized anarchies and bureaucracies. Heterogeneous motivation compounds coordination problems, but is usually less important than competence. Our findings point to matching and interactive learning as fruitful areas for further study.

In this study, the authors unpack the micro-level processes of knowledge accumulation (experiential learning) and knowledge application (problem solving) to examine how task allocation structures influence organizational learning. The authors draw on untapped potential of the classical garbage can model (GCM), and extend it to analyze how restrictions on project participation influence differentiation and integration of organizational members’ knowledge and consequently organizational efficiency in solving the diverse, changing problems from an uncertain task environment. To isolate the effects of problem or knowledge diversity and experiential learning, the authors designed three simulation experiments to identify the most efficient task allocation structure in conditions of (1) knowledge homogeneity, (2) knowledge heterogeneity, and (3) experiential learning. The authors find that free project participation is superior when the members’ knowledge and the problems they solve are homogenous. When problems and knowledge are heterogeneous, the design requirement is on matching specialists to problem types. Finally, the authors found that experiential learning creates a dynamic problem where the double duty of adapting the members’ specialization and matching the specialists to problem types is best solved by a hierarchic structure (if problems are challenging). Underlying the efficiency of the hierarchical structure is an adaptive role of specialized members in organizational learning and problem solving: their narrow but deep knowledge helps the organization to adapt the knowledge of its members while efficiently dealing with the problems at hand. This happens because highly specialized members reduce the necessary scope of knowledge and learning for other members during a certain period of time. And this makes it easier for the generalists and for the organization as a whole, to adapt to unforeseen shifts in knowledge demand because they need to learn less. From this nuanced perspective, differentiation and integration may have a complementary, rather than contradictory, relation under environmental uncertainty and problem diversity.

An important question facing business scholars is whether and how organizations may best adapt their investments, resource profiles, and strategies to the demands of their particular environments. While a broad literature describes organizational design principles that may assist in this regard, more recent work builds on Kauffman's (1993) NK model of biological evolution to explore how selection mechanisms and adaptive principles promote firms' exploitation and exploration efforts. This research stream has made contributions regarding the importance and efficacy of various internal adaptive factors in particular environmental settings. For instance, Levinthal (1997) shows that, despite extensive adaptation efforts, the influence of imprinting persists in complex environments with many local peaks. Rivkin (2000) demonstrates that NK complexity degrades the efficacy of search, compelling imitators to rely on search heuristics rather than adaptation via local learning. Rivkin and Siggelkow (2003) explore the tradeoffs between exploration and stability, and describe how particular organizational attributes, such as vertical hierarchy and group- or firm-level incentive systems, influence the flow of information throughout the organization. These as well as other contributions have added precision to the conceptualization of environments and sharpened understanding of organization by describing precisely how interdependencies across investment choices and/or resource profiles affect adaptation efforts.

Many organizational problems are poorly defined, emotionally laden, and ambiguous. These types of problems rarely have one right answer and the criteria for evaluating the appropriateness of solutions is likely to be context dependent. Further, although cognitive skills are important to effective problem solving, the nature of these problems may also require emotional skills as well. This chapter presents a study which set out to determine whether emotional intelligence as an ability contributes above and beyond cognitive intelligence to the quantity, flexibility, and quality of solutions generated to ill-structured problems. Although support was not found for the notion that emotional intelligence explains the indices of solution generation beyond that of cognitive intelligence, the findings did show that emotional intelligence was a significant predictor of one of the solution metrics, namely the average resolving power of solutions across the two problems. The findings demonstrate that emotional intelligence and cognitive intelligence are separate constructs and suggest that caution be used in proposing the pervasive effects of emotional intelligence. In particular, the results of this study suggest that emotional intelligence may not equally influence all activities, highlighting the need to investigate which steps of the problem-solving process it does indeed impact.

Morality is primarily a system of values, meanings, convictions, beliefs, principles, and drivers of good behavior and good outcomes in any organization. Using systems thinking concepts and applications introduced and developed during the last 50 years or so by various scholars from MIT, Stanford, and Wharton, such as Chris Argyris, Russell Ackoff, G. K. Forrester, Peter Senge, Stephen Covey, and Jim Collins, this chapter seeks to explore various past and contemporary market systems and challenges in terms of specific inputs, processes, and outputs. Systems thinking reckons everything in the cosmos (usually classified as subjects, objects, properties, and events) as a system (composed of two or more interactive parts with individual and interactive effects) that is connected to every other system in the universe. Various systems thinking laws and archetypes that have been developed thus far by systems thinkers will be introduced in order to identify basic patterns, structures, and constraints of human thinking and reasoning that create market phenomena. The academic and managerial challenge is to identify, explore, and capitalize such nonobvious connections for creating and developing new markets and corporate growth opportunities in the highly turbulent markets of today. In a globalized, digitized, and networked planet and universe, systems thinking is a very effective tool for analyzing turbulent market systems holistically and in an inclusive and integrated manner, with their specific inputs, processes, and outcomes. Several contemporary market cases will be included to illustrate the contents of this chapter.

While deficits for students with learning disabilities (LD) are prevalent in almost all aspects of mathematics, difficulty in the application and understanding of problem-solving tasks are much more challenging to remediate than computational and procedural skills. Given the complexities involved in authentic problem-solving activities emphasized in current mathematics standards and the inherent challenges presented to students with LD, the importance of using strategies and techniques guided by evidence-based practices is paramount. Yet, ineffective instructional strategies for problem solving are still widespread in both mathematics curricula and available teacher resources. In this chapter, we provide a description of a commonly used ineffective problem-solving strategy (i.e., the keyword strategy), an overview of the keyword research, and an explanation for its ineffectiveness. We conclude with a description of three evidenced-based problem-solving approaches and practices that significantly improve the mathematical performance of students with LD.

Contemporary literature reveals that, to date, the poultry livestock sector has not received sufficient research attention. This particular industry suffers from unstructured supply chain practices, lack of awareness of the implications of the sustainability concept and failure to recycle poultry wastes. The current research thus attempts to develop an integrated supply chain model in the context of poultry industry in Bangladesh. The study considers both sustainability and supply chain issues in order to incorporate them in the poultry supply chain. By placing the forward and reverse supply chains in a single framework, existing problems can be resolved to gain economic, social and environmental benefits, which will be more sustainable than the present practices.

The theoretical underpinning of this research is ‘sustainability’ and the ‘supply chain processes’ in order to examine possible improvements in the poultry production process along with waste management. The research adopts the positivist paradigm and ‘design science’ methods with the support of system dynamics (SD) and the case study methods. Initially, a mental model is developed followed by the causal loop diagram based on in-depth interviews, focus group discussions and observation techniques. The causal model helps to understand the linkages between the associated variables for each issue. Finally, the causal loop diagram is transformed into a stock and flow (quantitative) model, which is a prerequisite for SD-based simulation modelling. A decision support system (DSS) is then developed to analyse the complex decision-making process along the supply chains.

The findings reveal that integration of the supply chain can bring economic, social and environmental sustainability along with a structured production process. It is also observed that the poultry industry can apply the model outcomes in the real-life practices with minor adjustments. This present research has both theoretical and practical implications. The proposed model’s unique characteristics in mitigating the existing problems are supported by the sustainability and supply chain theories. As for practical implications, the poultry industry in Bangladesh can follow the proposed supply chain structure (as par the research model) and test various policies via simulation prior to its application. Positive outcomes of the simulation study may provide enough confidence to implement the desired changes within the industry and their supply chain networks.

This chapter outlines potential steps to take in designing active learning experiences based on several theories underlying the learning process. The chapter examines theories of learning and instruction including information processing, schema acquisition, and cognitive load theory. Next follows an explanation of how these theories support problem-centered learning as well as a rationale for the need to help learners develop domain-general, flexible problem-solving skills that will transfer to future needs and contexts. The second half of the chapter focuses on designing active learning experiences based on the selection of real-world problems as the foundation for learning, activating prior knowledge, demonstration of the process or concept, multiple opportunities for practice with relevant scaffolding, and the chance to integrate that knowledge into the learners’ own context based on M. D. Merrill’s (2002) First Principles of Instruction. Examples of assessments, strategies, and activities to foster active, problem-centered learning drawn from the literature are also provided.