The strategic value of ergonomics for companies

The discipline of ergonomics studies the interaction between man and the designed technical and organizational environment. In product ergonomics, this knowledge is used to develop user-friendly products and in production ergonomics to design human-friendly (production) processes. Beside social goals, ergonomics can contribute to economic goals of an organization. With user-friendly products, a company can deliver benefits to its customers, which exceed those of competing products. With human-friendly production processes, a company can increase labor productivity and consequently can reach important cost-reductions. The growing consciousness of the importance of humans (customers and workers) for the success of organizations, implies that ergonomics can have a strategic value for the management of organizations. In this paper a model is presented how ergonomics can be integrated in the decision-making and design processes of organizations, and examples are given to show possible business benefits.Competitive advantage, labor productivity, human centered design, product development, process development

Necessary Condition Analysis (NCA): Logic and Methodology of “Necessary but Not Sufficient” Causality

Theoretical “necessary but not sufficient” statements are common in the organizational sciences. Traditional data analyses approaches (e.g., correlation or multiple regression) are not appropriate for testing or inducing such statements. This article proposes necessary condition analysis (NCA) as a general and straightforward methodology for identifying necessary conditions in data sets. The article presents the logic and methodology of necessary but not sufficient contributions of organizational determinants (e.g., events, characteristics, resources, efforts) to a desired outcome (e.g., good performance). A necessary determinant must be present for achieving an outcome, but its presence is not sufficient to obtain that outcome. Without the necessary condition, there is guaranteed failure, which cannot be compensated by other determinants of the outcome. This logic and its related methodology are fundamentally different from the traditional sufficiency-based logic and methodology. Practical recommendations and free software are offered to support researcher

Necessary Condition Analysis (NCA) with R (version 3.2.0)

Necessary Condition Analysis (NCA) is an approach and data analysis technique for identifying necessary conditions in datasets. It can complement traditional regression-based data analysis as well as methods like QCA (see [the NCA website](https://www.erim.nl/nca) for more information on NCA). This guide helps a novice user without knowledge of R or NCA to install the free R and NCA software on the user’s computer and to perform an NCA analysis within 15 minutes. The main instructions are: I. Install R II. Install NCA III. Load data IV. Run NCA. Details of the method can be found in: - Dul, J. (2016) Necessary Condition Analysis (NCA). Logic and Methodology of 'Necessary but not Sufficient' causality. *Organizational Research Methods* 19(1), 10-52. [Sage](https://journals.sagepub.com/doi/pdf/10.1177/1094428115584005) - Dul, J. (2020), *Conducting Necessary Condition Analysis*, Sage Publications, ISBN: 9781526460141. [Sage](https://uk.sagepub.com/en-gb/eur/conducting-necessary-condition-analysis-for-business-and-management-students/book262898) - Dul, J., van der Laan, E., & Kuik, R. (2020). A statistical significance test for Necessary Condition Analysis. *Organizational Research Methods*, 23(2), 385-395. [Sage](https://journals.sagepub.com/doi/10.1177/1094428118795272

Necessary condition analysis: more value from data

Data is often surprisingly short on real answers for managers, particularly if a problem is multi-causal. Sometimes we can measure all the relevant factors in a complex system to the fourth decimal place and yet still have no clear sense of which factors matter

Building and testing necessity theories in supply chain management

This article contributes to the Emerging Discourse Incubator initiative by presenting how supply chain management scholars can contribute to theory development by means of necessity theories. These are unique theories that inform what level of a concept must be present to achieve a desired level of the outcome. Necessity theories consist of concepts that are necessary but not sufficient conditions for an outcome, where the absence of a single causal concept ensures the absence of the outcome. The theoretical features of necessary conditions have important implications for understanding supply chain management phenomena and providing practical applications. In 2016, Necessary Condition Analysis (NCA) became available for building and testing necessity theories with empirical data. However, NCA has not yet been used for the development of supply chain management theories. Therefore, we explain how necessity theories can be built and tested in a supply chain management context using necessity logic and the empirical methodology of NCA. We intend to inspire scholars to develop novel necessity theories that deepen or renew our understanding of supply chain management phenomena

An assessment system for rating scientific journals in the field of ergonomics and human factors

A method for selecting and rating scientific and professional journals representing the discipline of ergonomics and human factors is proposed. The method is based upon the journal list, impact factors and citations provided by the Institute of Scientific Information (ISI), and the journal list published in the Ergonomics Abstracts. Three groups of journals were distinguished. The "ergonomics journals" focus exclusively on ergonomics or human factors. The "related journals" focus on other disciplines than ergonomics or human factors, but regularly publish ergonomics/human factors papers. The "basic journals" focus on other technical, medical or social sciences than ergonomics, but are important for the development of ergonomics/human factors. Journal quality was rated using a maximum of four categories: top quality (A-level), high quality (B-level), good quality (C-level)) and professional (P-level). The above methods were applied to develop journal ratings for the year 2003. A total of 24 'ergonomics journals', 58 'related journals' and 142 'basic journals' were classified

Theory-Building With Cases

Theory-building with cases is (a) formulating new propositions that emerge from the empirical evidence in a sample of cases and (b) testing them in the same sample. The main difference with most other forms of generating new propositions (such as analyzing the theoretical literature, brainstorming, etc.) is its empirical character. The main difference with other forms of discovering new propositions in empirical evidence (such as in ‘exploratory’ research) is that only those theoretical formulations are accepted as a result of the theory-building study that are confirmed in a test in the sample from which the proposition was built. It is possible that a proposition about a relationship between two variables emerges from an exploratory single case study (e.g., when both variables have extreme values in that case), but it is not possible to test that new proposition in the same study because this would require a comparison in a sample of cases. The term theory-building study (as distinct from an exploratory study) is used here only for studies in which a proper test of the new proposition has been conducted

Work environments for employee creativity

Innovative organisations need creative employees who generate new ideas for product or process innovation. This paper presents a conceptual framework for the effect of personal, social-organisational and physical factors on employee creativity. Based on this framework an instrument to analyse the extent to which the work environment enhances creativity is developed. We apply this instrument to a sample of 409 employees and find support for the hypothesis that a creative work environment enhances creative performance. We illustrate how the instrument can be used in companies to select and implement improvements

Unraveling the dimensions of supplier involvement and their effects on NPD performance: a meta-analysis

We study the relationship between supplier involvement in New Product Development and performance. The current literature is scattered and fragmented with studies reporting mixed empirical evidence for a variety of concepts related to ‘Early Supplier Involvement’. We conduct a systematic review and meta‐analysis of the existing literature to reconcile conflicted findings, revise and refine theoretical perspectives, and provide evidence‐based scholarly and practical implications. To achieve these aims, we unravel the general relationship by considering three factors. First, we delineate different types of performance outcomes, mainly related to NPD efficiency (e.g., speed) and NPD effectiveness (e.g., product quality). Second, we distinguish between the moment and the extent of supplier involvement, related to different theoretical perspectives on external knowledge integration. Third, we disentangle multiple levels of analysis that are seemingly obscured in the literature, specifically the project and organizational levels. We find that extensive supplier involvement has positive effects on NPD efficiency and effectiveness, whereas earlier supplier involvement only to some degree affects NPD efficiency and not effectiveness. In conclusion, our meta‐analysis based on 11,420 observations from 51 studies provides strong theoretical and practical insights on the important phenomenon of supplier involvement

Pattern matching

Pattern matching is comparing two patterns in order to determine whether they match (i.e., that they are the same) or do not match (i.e., that they differ). Pattern matching is the core procedure of theory-testing with cases. Testing consists of matching an “observed pattern” (a pattern of measured values) with an “expected pattern” (a hypothesis), and deciding whether these patterns match (resulting in a confirmation of the hypothesis) or do not match (resulting in a disconfirmation). Essential to pattern matching (as opposed to pattern recognition, which is a procedure by which theory is built) is that the expected pattern is precisely specified before the matching takes place