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While managers and researchers agree that the fuzzy front end of new product development (NPD) is critical for project success, the meaning of the term “front‐end fuzziness” remains vague. It is often used broadly to refer to both the exogenous causes and the internal consequences of fuzziness. This imprecise language makes it difficult for managers to separate cause and effect and thus identify specific prescriptive remedies for “fuzziness” problems. The vagueness of the concept and the lack of a framework for defining “front‐end fuzziness” also impede empirical research efforts. Building upon uncertainty theory, we define front‐end fuzziness in terms of environmental uncertainties. Front‐end fuzziness has consequences for a project’s team vision. It reduces the team’s sense of shared purpose and causes unclear project targets and priorities. Describes how foundation elements of a firm’s overall product development program can help project teams cope with front‐end fuzziness.

Fuzziology explores the fuzziness inherent in what we know about ourselves and our experience, about our thoughts and feelings, drives for understanding and urges to create. By studying the fuzziness – its nature, sources, causes and factors affecting its dynamics, we are able to transcend the limitations, which it constantly puts on the processes of our understanding and knowing. The basic postulate and paradox of fuzziology are formulated together with its main principles, logical tools and theorems. The link between Socrates' maieutic inquiry, the ancient Vedic wisdom and fuzziology has helped us to reveal the significance of its central maxim: “Do not reject anything, but do not remain with anything either; go beyond!”

This article contributes to the fuzzy logic application literature in accounting by examining a key issue in the use of fuzzy logic: how to find an optimum number of classes to minimize the decision maker's cost. Two costs are assumed: (1) we assume fuzziness is costly and thus should be minimized and (2) we assume that adding categories is costly. In order to address the issue of finding the optimal number of classes, we define the objective function as being cost minimization. We seek to determine the costs and benefits of increasing the number of classifications and ask whether an internal optimum is identifiable and achievable. We assume, ceteris paribus, less fuzziness is preferable to more fuzziness, but fuzziness can only be reduced through the use of more categories whose creation is costly. More fuzziness is costly, but so is the creation of additional categories to alleviate the fuzziness. When we arrive at the optimal number of clusters that corresponds to a minimal total cost, that number may not be the same as the “natural” number of categories. It is, nonetheless, a useful and practical way of deciding on the number of classifications. The approach we employ in this study is not confined to a management accounting information environment. It can be applied to any information environment where measurable classifications exist.

The uncertainties, ambiguities and unknown issues that characterize the beginning of a new product development project have led practitioners and scholars to coin the term “fuzzy front end” to refer to this seminal stage. The causes of many product failures can be traced back to this fuzzy front end. Despite a growing realization of its importance, the meaning of the term “front-end fuzziness” itself remains vague. A theory-based framework is not available to guide thinking and research on this important topic. The purpose of this paper is to create a conceptual framework for fuzzy front end.

This paper applies Daft and Lengel’s (1986) theory of organizational information processing to create a framework for research on the fuzzy front end of product innovation. This framework is used to clarify the concept of front-end fuzziness and identify three sources of fuzziness in the project team’s task environment.

Contrary to the current literature, the authors argue that equivocality rather than uncertainty is the dominant cause of front-end fuzziness. This new conceptualization: appropriately broadens the concept of front-end fuzziness; identifies new problem areas; highlights the need for new solutions; and suggests the need to focus on team vision as a front-end deliverable.

The previous literature used a single uncertainty reduction rationale for integrative mechanisms. Thus, it did not consider that the implementation might change from front end to downstream. The dual theoretical rationale suggests that integrative mechanisms can be implemented differently to focus either on uncertainty or equivocality reduction. In the front end, equivocality is the dominant issue and mechanisms should be implemented in ways that create a rich channel to identify issues and share perspectives. In downstream activities where the dominant issue is uncertainty reduction, mechanisms might be implemented in ways that provide greater quantities of information on known issues.

This new conceptualization of front-end fuzziness provides a better theoretical rationale for how integrative mechanisms enable a project team to work through the fuzziness it faces, creating a clear team vision that guides its downstream efforts.

Melting universality, quantification and relative computability into a meta‐synthesis, pansystems theory develops an investigation on W‐fuzziness and 0*‐fuzziness connected with generalized conceptions such as derivative, equation, variational principle and OR. The purpose of this paper is to unify various mathematical structures, fuzziness categories, definitions of systems are unified within a general framework.

The paper includes topics: pansystems approach to fuzzy systems and relations, pansystems variational principle and Zadeh's extension principle, pansystems clustering and its fuzzy embodiment, pansystems topology and approximation to fuzziness, relative unification of fuzziness and roughness.

Zadeh extension principle about fuzziness transmission can be considered as a specific model of pansystems extremum principle, and so the more modes can be developed. Based on them a further investigation is present on pansystems clustering, which is a W‐fuzzy clustering, an extension or sublation of traditional one and fuzzy one.

Pansystems clustering embodies mutuality of many logoi of different subbraches with classification‐styled OR, including related interpromotions of the principles among knowledge rediscovery, data mining, mathematical reasoning and the investigations of fuzzy systems. W‐fuzziness and 0*‐fuzziness realize a relative unification for many logoi and principles.

Project management scholars and practitioners have long debated how best to harness social interactions to optimise knowledge exchange and enhance stakeholder alignment and value. This study aims to assist project managers to understand and manage fuzziness and create enduring front-end value. It views the project life cycle as a potential source of co-created value. The paper uses a social capital lens to provide a deeper understanding of the project front-end; it uses a three-dimensional view (structural, relational, cognitive) to explore how stakeholder social capital can overcome front-end fuzziness to enhance decision-making and, thus, value creation.

Semi-structured interviews were conducted with senior managers from teleconnections companies, which, when combined with secondary data, established the impact, nature and dimensions of social capital within a project management setting.

The research found that social capital can help to reduce complexity, uncertainty and equivocality in the early stages of projects, making them more clearly defined and thus helping to create greater stakeholder value in the later stages of the project. A surprising finding was that some project team members engaged in intentional equivocality to try to promote their own benefits rather than those of the organisation.

This paper reconceptualises the impact of social capital on stakeholder value creation in the front-end of projects. The paper contributes to a more holistic view of the front-end of project management, focusing social capital to reduce the sources of front-end fuzziness.

Fabric fuzziness is a property which affects the appearance, handle, thermal insulation and other fabric features; it also leads to pilling that is a serious problem for the apparel industry. Fuzz on fabric surface has been measured mostly by subjective methods (human vision) rather than objective methods. In this study, an objective method using image analysis techniques is developed for the measurement of fuzz on fabric surface. The effects of fibers blend ratio and abrasion on fabric fuzziness are evaluated. For this purpose, several blended plain fabrics including polyester/viscose fibers with different blend ratios were produced and abraded under various cycles of traversing abrasion. Yarn hairiness was also measured using an Uster tester 4 apparatus before weaving. High correlation between Uster 4 data and the results obtained from the fuzz measurement of fabrics demonstrates that this method has high accuracy and great potential for the determination of fabric fuzziness levels in a quantitative and reliable manner.

The purpose of the paper is to conceptually elaborate two important mechanisms, authenticity and fuzziness, that affect how audiences react to deviations from existing genres by artists that are making their first entry. In cultural industries such as music, social categorization systems play an important role in the success of actors. Audience members evaluate entering artists vis‐à‐vis the existing, collective system of categories and related normative social codes, and may or may not impose penalties for code violations.

This is a conceptual paper and the conceptual framework is built on recent theorization regarding social categories in organizational fields. A key premise is that such categories, including musical genres, are fuzzy with blurred boundaries and partial membership. Such fuzziness is likely to affect organizational viability and dynamics.

Based on the conceptualization, the baseline proposition is that artists making their first entry are likely to face higher penalties by audiences if they deviate from existing genres. However, the higher the idiosyncratic authenticity of an artist, the smaller such penalties are. Moreover, we expect penalties to be smaller when genre fuzziness increases.

Besides contributions to theory, the propositions that are stated in the paper should have relevance to record companies and artists when they are making strategic decisions regarding artist identity upon first entry.

The paper offers a novel perspective to extant research in music regarding genres, categories, and organizational identities. Furthermore, the paper contributes to recently emerged sociological theory on fuzzy categories and authenticity.

Addresses the problem of matching two fuzzy sets. Proposes a matching method that considers the extent to which both fuzzy sets have the same meaning. For a given degree of similarity between two sets, the same meaning decreases as the fuzziness increases and, in particular, for equal fuzzy sets the degree of matching is a function of the fuzziness only. A complete matching of two sets is obtained only when they are equal and crisp. Finally, the inverse problem is studied, of characterizing one of the sets used in the match when knowing the other set and the result of the matching.

Understanding when new forms will emerge constitutes a core theoretical issue for organizational theory. The ecological theory of form emergence falls short of providing a full explanation because it treats legitimation as a primitive (unexplained) concept. Here, we use Hannan, Pólos, and Carroll's (2007) revised theory of organizational evolution to interpret and respecify the legitimation part of the density dependence model. Among other advantages, the respecification allows us to incorporate the insights of the “cultural-frame” institutional perspective. We study early Dutch accounting, an industry setting where form legitimation was fiercely contested by several professional associations in the period 1884–1939. We develop an analytical narrative about the historical legitimation process, and we also present systematic tests of the theory examining predictions about “fuzzy” density and population contrast. Estimated models of firm exit support the revised theory and reveal that fuzziness, induced from fragmented collective action, hampers it.