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Market participants form conceptualizations of the products exchanged within product markets. Strategy scholars have begun to investigate how these product conceptual systems influence firm strategic behavior. Much of this work characterize these concepts as categories and theorize that the strategic implications derive from the potential penalties of not fitting into a category. This view has limitations in that it does not fully address the other cognitive tasks that concepts perform as well as other system-level characteristics of the conceptual systems. This chapter addresses these limitations by framing the use of concepts as part of the interpretive processes that enable market exchange. It develops a system-view of product concepts and then shows how the structure of the product categorical system influences the interpretation of product concepts. It introduces new mechanisms centered on cognitive processing that influence strategic action within product markets.

Draws on the works of a number of authors to synthesize a conceptual framework (or, as we prefer, a linguistic‐conceptual system) within which to think about conceptual frameworks; indicates within that system what conceptual frameworks “are”; describes within that system what it “is” that we do when we appropriate them; specifies within that system what it “is” which we do when we apply them; suggests ways in which thinking about conceptual frameworks in the manner indicated may contribute to the development of competence in their appropriation and skilful application; points out the ways in which that competence might be expected to contribute to increased problem‐solving effectiveness, and identifies several implications for instruction. Suggests that the “theory‐practice gap” arises as a partial consequence of our failure to conceptualize adequately the “nature” of conceptual frameworks and the manner in which their use can enhance effectiveness in the analysis and reformulation of unstructured problems.

A general theory of conceptual systems (GCST) is developed, in which the concept of systems is introduced not a priori or theoretically, but by means of a rigorous procedure, supported by experimental knowledge of these or other system realization forms (so‐called “realization” and “trajectories”). This is the stage of an inductive construction of computable concepts, i.e. “concepts”. “Concept” is presented in the form of a function of discrete variables, one part of which is known (essential variables), while the other part is independent (non‐essential variables). The presence of “concept” allows one to construct new, not yet realized “trajectories”. This is the stage of the deductive conclusion. The general principles of GCST construction on the basis of an artificial intelligence program system are formulated. This approach excludes widespread methods of a priori construction of system theories (e.g. in the “theory of usefulness”, the “study of operations”).

The complexity of the modern world calls for the increasingly complex (i.e. containing more concepts) and systemic (i.e. containing more causal connections between the concepts) conceptual systems, such as theories and mental models which may exist at varying levels of complexity and systemicity. Yet, these systems are often found to be impervious to data and counter-arguments. Examples of such disputes are found in arguments over global warming and in the many debates between political groups. The purpose of this paper is to review the reasons behind this imperviance and identify ways to move forward.

The paper brings together the insights from the burgeoning science of conceptual systems as well as selected ideas from the moral philosophies of Niklas Luhmann and Jürgen Habermas. The science of conceptual systems is utilized to unearth the cognitive reasons for the imperviance of conceptual systems, while the work of Luhmann and Habermas is brought to bear on the moral reasons.

The most salient cognitive reasons for this imperviance are shown to be related to the questionable validity of data, the situational inappropriateness of conceptual systems, as well as their low complexity and systemicity. The effect of the moral content of conceptual systems on their imperviance is ambivalent. For Luhmann, moral communication may enhance imperviance and induce conflicts. In contrast, the Habermasian discourse ethics may counteract imperviance by stimulating the rational moral argumentation.

The science of conceptual systems is uniquely positioned to analyze the pervasive problem of their imperviance, especially if this problem is aggravated by moral reasons.

To explore the value and the case for designing antifragile socio-technical information systems (IS) in an era of big data, moving beyond traditional notions of IS design towards systems that can leverage uncertainty for gains.

A design science research (DSR) approach was adopted, comprising four stages, including problem identification and solutions definition, conceptual artifact or socio-technical system design, preliminary evaluation, and communication and knowledge capture.

A conceptual socio-technical artifact that identifies antecedents to antifragile IS design. When operationalised, the antecedents may produce the desired antifragile outcome. The antecedents are categorised as value propositions, design decisions and system capabilities.

This research is conceptual in nature, applied and evaluated in a single big data analytics case study in Facebook-Cambridge Analytica. Future research should empirically validate across a range of real-world big data contexts, beyond the presented case study.

Uncertainty generally results in socio-technical system failures, impacting individuals, organisations and communities. Conversely, antifragile IS can respond favourably to the shocks and stressors brought forth by periods of elevated uncertainty.

Antifragile IS can drive socio-technical systems to respond favourably to uncertainty and stressors. Typically, these socio-technical systems are large, complex structures, with increased connectivity and the requirement to generate, process, analyse and use large datasets. When these systems fail, it affects individuals, organisations and communities.

Existing IS design methodologies and frameworks largely ignore antifragility as a possible designable outcome. Extant research is limited to abstract architectural design, and approaches based on the proposition of principles. This research contributes to knowledge of antifragile IS design, by deriving a conceptual artifact or socio-technical system based on antecedent-outcome relationships that leverage uncertainty towards performance gains.

The purpose of this paper is to describe the framework of conceptual design support tool (CDST) developed to assist designers during conceptual design process. The premise of the study is that, by combining human creativity with computer capabilities, it is possible to perform conceptual design process more efficiently than solely manual design. The study aims to show how conceptual design knowledge can be captured from experienced designers and kept in the computer system for later use and how the developed tool assists designers by handling some of the repetitive and time‐consuming tasks.

A conceptual design process model, which integrates systematic design approach with knowledge‐based system, is proposed. Based on this model, a CDST, which consists of function library, alternative concepts database, different modules for conceptual design activities, and a knowledge‐based system is developed. The alternative concepts database is built based on design reuse philosophy. Furthermore, the tool is designed to accept and save new concepts from the user through its knowledge acquisition module without modifying the source code. The CDST is developed using public domain open source programming environments namely CLIPS, Python, wxPython, and PyCLIPS.

Through its graphical user interface, CDST assists designers in performing the conceptual design process such as functional modelling, using standard vocabularies of functions, generating concepts and displaying on morphology chart, concept combination, and concept evaluation. The functionality and interaction between the user and the CDST is demonstrated with an example.

Currently, the alternative concepts database consists of concepts from subsea process equipment design and few general mechanical designs. The database can be enhanced by adding more concepts through the knowledge acquisition module provided.

The tool can be used as a knowledge management system in industry by capturing expertise knowledge and to train novice designers. It augments designer's knowledge by providing concepts from past designs.

The research output from this paper can be valuable resource in industry to support designers with computers. The research represents one of the attempts to develop domain independent conceptual design tool that can acquire new concepts throughout its lifetime.

In collaborative settings, such as research and development projects, obtaining the maximum benefit from knowledge management systems depends on the ability of the different partners to understand the conceptualisation underlying the system’s knowledge organisation. This paper aims to show how information/knowledge organisation in a multi-organisation project can be made more effective if the domain experts are involved in the specification of the systems semantic structure. A particular aspect is further studied: the role of conceptual relations in the process of collaborative development of such structures.

An action-research approach was adopted, framed by a socio-semantic stance. A collaborative conceptual modelling platform was used to support the members of a research and development project in the process of developing a lightweight ontology aiming at reorganising all the project information in a wiki system. Data collection was carried out by means of participant observation, interviews and a questionnaire.

The approach to solve the content organisation problem revealed to be effective both in the result and the process. It resulted in a better-organised system, enabling more efficient project information retrieval. The collaborative development of the lightweight ontology embodied, in fact, a learning process, leading to a shared conceptualisation. The research results point to the importance of the elicitation of conceptual relations for structuring the project’s knowledge. These results are important for the design of methods and tools to support the collaborative development of conceptual models.

This paper studies the social process leading to a shared conceptualisation, a subject that has not been sufficiently researched. This case study provides evidence about the importance of the early phases of the construction of ontologies, mainly if domain experts are deeply involved, supported by appropriated tools and guided by well-structured processes.

The aim of this paper is to develop the conceptual design of an information and communication technologies (ICT) platform supporting the inbound open innovation (OI) process within the technological developments business unit of Leonardo Defence Systems.

After a preliminary phase concerning the context analysis, methodology includes three main steps: conceptualization of functions; preliminary design; and conceptual design of the system/SW architecture. In each of these phases, the authors tried to merge evidence from the scientific literature with empirical insight emerging from the field.

Results report the conceptual design proposal for an integrated ICT platform supporting the OI. It includes the conceptualization of main functions, the preliminary design deriving from use cases and the proposal for the overall system architecture and data model.

The research focuses only on the conceptual design phase; at this stage, the platform has not been still implemented or tested. Also, generalizability concerns may arise from the single-application context.

The outcoming conceptual design can be useful for firms that open their boundaries to external partners, as well as for software developers which could draw on it. Firms approaching similar OI challenges can re-contextualize the platform to their own setting.

Originality of this research relies on the attempt to show how ICT can support firms in their OI processes and, secondly, to support firms aiming to create a positive environment that encourages people at leveraging existing external technological opportunities and sources of knowledge. In so doing, a systematic design approach to the definition of the conceptual proposal is also pursued.

The conceptual synthesis of the accepted bodies of knowledge in a culture provides a sense of meaning in existence, a viable image of the future, and individual and collective motivations. In our age, the dominant bodies of knowledge are fragmented and, although they are more accurate than ever before in limited domains, they fail to guide the imagination and inspire purposive action. To rectify this situation we need to develop a coherent and explicit conceptual synthesis that is based on science but extends beyond the current range of validated scientific theories, overcoming the noxious separation of the factual and the moral, the empirical and the mystical, the sensate and the affective. General systems theory, a metadiscipline created specifically for the purpose of integrating scientific research and theories, is a highly qualified instrument for promoting the required science‐based conceptual synthesis. Efforts in this direction are aided by the intrinsic tendency within science to correct for overspecialization and fragmentation through a search for integrative general theories, and by the rising perception of societal need for integrated bodies of knowledge, capable of coping with the increasing complexity of contemporary problems.

Identifies the significant role of conceptual research methods in theory building and contrasts it with the theory‐testing research currently prevalent in operations management. The research stages of description, explanation and testing are used to distinguish between theory building and theory testing. Short‐circuiting any one of these stages results in dysfunctional research activities which produce war stories, black boxes, or ivory‐tower prescriptions. Defines some terms relevant to conceptual research methods and describes different conceptual classification schemes. Finally, discusses the differences between conceptual models, frameworks, and theories and illustrates each method with examples from the literature.