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Via philosophy of science, the paper seeks to identify the role of trial and error in business and other managed activities, including economic development.

Drawing first on Karl Popper's view of trial and error as essential to the evolution of all life, including all human activity, the paper asks what we mean by science, and how distinctive is the scientific mode of enquiry. It goes on to look at, in particular, the treatment of trial and error, and of evolution, in two best‐selling management books of the last 25 years, and at the relevance of this treatment to some recent discussions of economic development.

Popper's distinction between science and various types of non‐science is not as clear‐cut as is sometimes portrayed. Moreover, there are significant variants to Popper's view of scientific method. But accepting Popper's view of the centrality of trial and error for problem solving the paper finds significant echoes in some management thinking. Related questions occur, also, in some recent discussions of economic development.

The paper argues that trial and error is a well‐established approach in business and other managed activities, and that there is potential to learn from many examples of success and disappointment.

The paper questions whether some views of science, and of the demarcation between science and non‐science, are as clear‐cut as is sometimes assumed. It argues against claimed novelty in some recent discussion of evolutionary process in business and elsewhere.

Applied Behavior Analysis (ABA) is a scientific method for modelling human behavior, successfully applied in the context of Autism. Recording and sharing measurable data (on subjects’ performance) between caregivers guarantees consistency of learning programs and allows monitoring the learning enhancements. Data are usually recorded on paper, which requires considerable effort and is subject to error. The purpose of this paper is to describe a portable application developed to support ABA tutors in their work with autistic subjects. It allows gathering data from ABA sessions, giving tutors rapid access to information, also in graphical formats.

The tool was designed via participatory design. Various ABA team members were involved, in order to make the application respond perfectly to their needs. The approach aims to ensure maximum usability, while minimizing errors and ambient interference.

The use of mobile devices (i.e. tablets or smartphones) allows mobility and ease of interaction, enabling efficient data collection and processing. Data plotting allows one to easily interpret gathered data.

The proposed application, free open source software, can be a valuable aid for supporting the ABA intervention and favor the inclusion of children with autism.

Available software to assist tutors during therapy sessions is often proprietary, and research prototypes are not freely available, so paper forms are still widespread. Besides, without attention to usability requirements, assisting tools would be comparable in efficiency with data insertion on paper. Our software was specifically designed following ABA principles and favors efficient data entry allowing natural interaction with touch screen interfaces: drag and drop, taps and gestures. Furthermore, it is shared in the public domain.

Through a series of critical discussions on Karl Popper’s evolutionary analysis of learning and the non-authoritarian values it promotes, the purpose of this paper is to advocate a Popperian approach for building medical student knowledge. Specifically, it challenges positivist assumptions that permeate the design and management of many educational institutions, including teaching hospitals, by considering what does and does not happen when learning takes place.

To illustrate how Popper’s approach differs from such a conception of learning, the paper examines the exchange between a preceptor (Sam) and a medical student (Lisa). The following exchange is based on the observations during a team meeting in a Canadian teaching hospital. The authors sent the transcript of the observation to Lisa for her comments. The statements in italics represent Lisa’s additions. Pseudonyms are used to protect the identity of participants in the exchange.

Popper’s evolutionary analysis of learning and the Objective Knowledge Growth Framework provide a means of managing specific aspects of one’s education through engaging in this learning process. Although this approach to teaching and decision making takes time to master, it does not require reconstituting existing institutional arrangements before it can be implemented in hospitals. Instead, it asks medical students, teachers and practitioners to be open to the theoretical underpinnings of the approach and to view knowledge growth as a process of systematic trial and error elimination.

This paper is original in its conceptualisation and may well become a classic in education circles. It draws on Popper’s philosophical arguments and enters into a much needed discourse for teaching and learning.

The purpose of the paper is to analyze cybernetic necessities of output‐side attention directing systems, i.e. how systems can decide to act towards one of various inputs.

Complex pattern recognition and sequence learning systems may recognize more than one pattern and deliver more than one output at a point in time. Therefore, they require an output‐side attention directing system to decide to act towards just one pattern. The necessary cybernetic structures of such systems are analyzed using a functional approach.

An output‐side attention directing system has to evaluate the effect of current observations (patterns, sequences, etc.) on highest level goal‐values (in a living system these are existential goal‐values like a body temperature or energy supply). Measure of this effect is the degree of goal‐approximation towards these goal‐values. This measure can either be preprogrammed for some patterns or sequences, or has to be determined in trial and error processes for new patterns or sequences learned by the system.

The paper shows the cybernetic necessities of the development of the “know how” of sequence learning systems in time, starting with default behavior, via learning new patterns and sequences, and trial and error to develop goal‐orientated actions towards them, until finally the achieved results enable experience based directing of attention.

The paper shows basic cybernetic structures and functions for output‐side attention directing systems required for all complex pattern recognition and sequence learning systems.

The purpose of this paper is to investigate how a substantial organization gradually builds a management accounting system from scratch, changing its accounting routines by learning processes. The paper uses the experiential learning theory and the concept of learning style to investigate the learning process during management accounting change. The study aims to expand the domain of management accounting change theory to emphasize the learning-related aspects that can constitute it.

The paper provides an interpretation of management accounting change based on the model of problem management proposed by Kolb (1983) and the theory of experiential learning (Kolb, 1976, 1984). The study is based on a 14-year longitudinal case study (1994‐2007). The case examined can be considered a theory illustration case. Data were obtained from a broad variety of sources including interviews, document analysis and adopting an interventionist approach during the redesign of the costing system.

The paper contributes to two important aspects of management accounting change. First, it becomes apparent that the costing information change was not a discrete event but a process of experience and learning conducted through several iterations of trial-and-error loops that extended over the years. Second, the findings reveal that the learning process can alter management accounting system design in a radical or incremental way according to the learning style of the people involved in the process of change.

Because of the adopted research approach, results could be extended only to other organizations presenting similar characteristics. Several further areas of research are suggested by the findings of this paper. In particular, it would be of interest to investigate the links between learning styles and communication and its effect on management accounting change.

The paper includes implications for the management of learning during management accounting change, to improve the efficiency and effectiveness of this process.

This paper is one response to the call for an interdisciplinary research approach to the management accounting change phenomena using a “method theory” taken from the discipline of management to provide an explanation of the change in management accounting. In respect of the previous literature, it provides two main contributions, namely, the proposal of a model useful both to interpret and manage learning processes; the effect of learning style on management accounting routines change.

What has been called “the McDonaldization of universities” (another name for top-down and strong corporate managerialism) has gained momentum as a model for governing and managing universities. This trend exacerbates the traditional tension between academic freedom and managerial control – a major challenge for the administration of academic institutions. The ideas of Charles Darwin represent an opportunity for overcoming such a challenge. However, traditional managerial models show inadequate, pre-Darwinian assumptions for devising organizational designs. This paper aims to show not only the opportunities but also the challenges of embracing a Darwinian paradigm for designing social systems. The case of managerialism in universities is an illustrative example. The paper proposes evolutionary guidelines for designing universities capable of maintaining managerial control while warranting academic freedom.

The paper proposes to understand the tension between academic freedom and managerial control in universities as the same tension between freedom and control that Karl Popper identified as successfully handled by evolutionary processes. The paper uses Darwinian theory, understood as a broader theory for complex systems, as a heuristic for designing social systems – universities in this case – able to adapt to changing environmental conditions while handling equilibrium between freedom and control. The methodology articulates the Popperian model of knowledge with the Darwinian scheme proposed by David Ellerman known as “parallel experimentation” for suggesting organizational forms in which university administrators and faculty can interact for generating free innovations in pseudo-controlled organizational arrangements.

A salient characteristic of strong managerialism is its pre-Darwinian understanding of survival and adaptation; such an approach shows important flaws that can lead universities to unfit designs that changing environments can select for elimination. As an alternative, the philosophy behind the ideas of Charles Darwin provides guidelines for designing innovative and adaptive social systems. Evolutionary principles challenge basic tenets of strong managerialism as Darwinian designs discard the possibility of seeing managers as knowledgeable designers that allegedly can avoid mistakes by allocating resources to “one-best” solutions through ex ante exhaustive, top-down control. Instead, a Darwinian model requires considering survival as a matter of adaptability through continuous experimentation of blind trials controlled by ex post selection. The key is to organize universities as experimenting systems that try new and different things all the time and that learn and improve by making mistakes, as an adaptive system.

Governing and managing universities require to acknowledge the uniqueness of academic institutions and demand to look for appropriate forms of organization. The proposal of this paper opens possibilities for exploring and implementing action-research initiatives and practical solutions for universities. Studies in management and administration of higher-education institutions must take into account the characteristics of this type of organizations and should consider wider spectrums of possibilities beyond the core ideas of managerialism.

University managers face a special challenge for achieving equilibrium between managerial control and academic freedom. Darwinian models of management invite to reconsider several management creeds, for instance, that “errors are bad things” – instead of innovation triggers and learning opportunities or that “one solution must fit all” – instead of considering bottom-up, different and adaptive solutions triggered by local academic units, each facing different environments.

Currently, there is no clear picture for governing universities. This paper introduces principles and guidelines for facing the current challenge that strong managerialism represents if universities are expected to maintain academic freedom and also survive in volatile environments.

The purpose of this paper is three-fold: to review the categories explaining mainly optimization algorithms (techniques) in that needed to improve the generalization performance and learning speed of the Feedforward Neural Network (FNN); to discover the change in research trends by analyzing all six categories (i.e. gradient learning algorithms for network training, gradient free learning algorithms, optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks, metaheuristic search algorithms) collectively; and recommend new research directions for researchers and facilitate users to understand algorithms real-world applications in solving complex management, engineering and health sciences problems.

The FNN has gained much attention from researchers to make a more informed decision in the last few decades. The literature survey is focused on the learning algorithms and the optimization techniques proposed in the last three decades. This paper (Part II) is an extension of Part I. For the sake of simplicity, the paper entitled “Machine learning facilitated business intelligence (Part I): Neural networks learning algorithms and applications” is referred to as Part I. To make the study consistent with Part I, the approach and survey methodology in this paper are kept similar to those in Part I.

Combining the work performed in Part I, the authors studied a total of 80 articles through popular keywords searching. The FNN learning algorithms and optimization techniques identified in the selected literature are classified into six categories based on their problem identification, mathematical model, technical reasoning and proposed solution. Previously, in Part I, the two categories focusing on the learning algorithms (i.e. gradient learning algorithms for network training, gradient free learning algorithms) are reviewed with their real-world applications in management, engineering, and health sciences. Therefore, in the current paper, Part II, the remaining four categories, exploring optimization techniques (i.e. optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks, metaheuristic search algorithms) are studied in detail. The algorithm explanation is made enriched by discussing their technical merits, limitations, and applications in their respective categories. Finally, the authors recommend future new research directions which can contribute to strengthening the literature.

The FNN contributions are rapidly increasing because of its ability to make reliably informed decisions. Like learning algorithms, reviewed in Part I, the focus is to enrich the comprehensive study by reviewing remaining categories focusing on the optimization techniques. However, future efforts may be needed to incorporate other algorithms into identified six categories or suggest new category to continuously monitor the shift in the research trends.

The authors studied the shift in research trend for three decades by collectively analyzing the learning algorithms and optimization techniques with their applications. This may help researchers to identify future research gaps to improve the generalization performance and learning speed, and user to understand the applications areas of the FNN. For instance, research contribution in FNN in the last three decades has changed from complex gradient-based algorithms to gradient free algorithms, trial and error hidden units fixed topology approach to cascade topology, hyperparameters initial guess to analytically calculation and converging algorithms at a global minimum rather than the local minimum.

The existing literature surveys include comparative study of the algorithms, identifying algorithms application areas and focusing on specific techniques in that it may not be able to identify algorithms categories, a shift in research trends over time, application area frequently analyzed, common research gaps and collective future directions. Part I and II attempts to overcome the existing literature surveys limitations by classifying articles into six categories covering a wide range of algorithm proposed to improve the FNN generalization performance and convergence rate. The classification of algorithms into six categories helps to analyze the shift in research trend which makes the classification scheme significant and innovative.

The purpose of this paper is to clarify how a competitive company develops its own method to create innovation by utilizing imitation and learning.

The paper examines the case of Fast Retailing from the viewpoint of imitation strategy.

Fast Retailing constantly explores and imports business ideas, evolves its business model through trial and error and finally creates innovation.

The paper emphasizes the importance of imitation strategy that flexibly accepts and extends business ideas through learning, creates new values by evolving a business model and combines them with corporate identity and brand.

The case study of Fast Retailing suggests that the successful imitation is enabled by flexible corporate culture and redefining its corporate identity and brand through the process of evolving its business model.

The purpose of this paper is to conduct a comprehensive review of the noteworthy contributions made in the area of the Feedforward neural network (FNN) to improve its generalization performance and convergence rate (learning speed); to identify new research directions that will help researchers to design new, simple and efficient algorithms and users to implement optimal designed FNNs for solving complex problems; and to explore the wide applications of the reviewed FNN algorithms in solving real-world management, engineering and health sciences problems and demonstrate the advantages of these algorithms in enhancing decision making for practical operations.

The FNN has gained much popularity during the last three decades. Therefore, the authors have focused on algorithms proposed during the last three decades. The selected databases were searched with popular keywords: “generalization performance,” “learning rate,” “overfitting” and “fixed and cascade architecture.” Combinations of the keywords were also used to get more relevant results. Duplicated articles in the databases, non-English language, and matched keywords but out of scope, were discarded.

The authors studied a total of 80 articles and classified them into six categories according to the nature of the algorithms proposed in these articles which aimed at improving the generalization performance and convergence rate of FNNs. To review and discuss all the six categories would result in the paper being too long. Therefore, the authors further divided the six categories into two parts (i.e. Part I and Part II). The current paper, Part I, investigates two categories that focus on learning algorithms (i.e. gradient learning algorithms for network training and gradient-free learning algorithms). Furthermore, the remaining four categories which mainly explore optimization techniques are reviewed in Part II (i.e. optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks and metaheuristic search algorithms). For the sake of simplicity, the paper entitled “Machine learning facilitated business intelligence (Part II): Neural networks optimization techniques and applications” is referred to as Part II. This results in a division of 80 articles into 38 and 42 for Part I and Part II, respectively. After discussing the FNN algorithms with their technical merits and limitations, along with real-world management, engineering and health sciences applications for each individual category, the authors suggest seven (three in Part I and other four in Part II) new future directions which can contribute to strengthening the literature.

The FNN contributions are numerous and cannot be covered in a single study. The authors remain focused on learning algorithms and optimization techniques, along with their application to real-world problems, proposing to improve the generalization performance and convergence rate of FNNs with the characteristics of computing optimal hyperparameters, connection weights, hidden units, selecting an appropriate network architecture rather than trial and error approaches and avoiding overfitting.

This study will help researchers and practitioners to deeply understand the existing algorithms merits of FNNs with limitations, research gaps, application areas and changes in research studies in the last three decades. Moreover, the user, after having in-depth knowledge by understanding the applications of algorithms in the real world, may apply appropriate FNN algorithms to get optimal results in the shortest possible time, with less effort, for their specific application area problems.

The existing literature surveys are limited in scope due to comparative study of the algorithms, studying algorithms application areas and focusing on specific techniques. This implies that the existing surveys are focused on studying some specific algorithms or their applications (e.g. pruning algorithms, constructive algorithms, etc.). In this work, the authors propose a comprehensive review of different categories, along with their real-world applications, that may affect FNN generalization performance and convergence rate. This makes the classification scheme novel and significant.