Pioneering Process Research: Andrew Pettigrew's Contribution to Management Scholarship, 1962–2014

This paper assesses Andrew Pettigrew's contribution to management scholarship. This review addresses the process, content, and context of his research career. Chronologically, the process will be subdivided into three distinct phases: the period leading up to the establishment of the Centre for Corporate Strategy and Change, Pettigrew's time with the Centre, and Pettigrew's research since leaving the Centre. The content of Pettigrew's research focussed on big problems and emerging phenomena such as decision‐making, organisational culture, organisation development, strategic change, human resource management, competitiveness, new public management, boards of directors, innovative forms of organising, high‐performing research teams, and business schools. His contextualist methodology for process research will be explicated. Pettigrew's contribution will be contextualised by comparing it with contemporary research. The paper concludes that there is still a need not only to examine big problems and emerging phenomena but also to provide a processual understanding of management reality. There is a need to further develop process research methodologies such as Pettigrew's contextualism, especially with respect to process research methods.     

Transient detailed balance and product form for reaction networks

This paper explores the boundary of the set of reaction networks that have an exact transient (truncated) multidimensional Poisson or product-form distribution for the number of particles of different types. Motivated by the birth–death process, we introduce the notions of transient detailed balance and delay functions, and use these notions to obtain the novel transient product-form distribution in a coagulation-fragmentation process for polymers with a tree-like structure from that of the pure coagulation process.     

Unraveling quantity discounts

We consider the situation in which a buying organization deals with a discrete quantity discount schedule offered by a selling organization. Furthermore, the buying organization can negotiate with the selling organization about the lot size and purchase price, but does not know the underlying function that was used by the selling organization to determine the quantity discount schedule. In this paper, we provide an analytical and empirical basis for one general quantity discount function (QDF) that can be used to describe the underlying function of almost all different quantity discount types. We first develop such a QDF analytically. Among other things, this QDF enables buying organizations to calculate detailed prices for a large number of quantities. We subsequently show that the QDF fits very well with 66 discount schedules found in practice. We discuss that the QDF and related indicators can be a useful tool in supplier selection and negotiation processes. It can also be used for competitive analyses, multiple sourcing decisions, and allocating savings for purchasing groups. Additionally, the QDF can be included in research models incorporating quantity discounts. We conclude the paper with an outlook on further QDF research regarding the characterization of commodity markets from a demand elasticity point of view.     

Further Beyond the Basic Background Check: Predicting Future Unethical Behavior

In this paper, we analyze the ethical issues of using honesty and integrity tests in employment screening. Our focus will be on the United States context: legal requirements related to applicant privacy differ in other countries, but we posit that our proposed balancing test is broadly applicable. We start by discussing why companies have ethical and legal obligations, based on a stakeholder analysis, to assess the integrity of potential employees. We then move to a consideration of how companies currently use background checks as a pre‐employment screening tool, noting their limitations. We then take up honesty and integrity testing, focusing particularly on the problems of false positives and due process. We offer a balancing test for the use of honesty and integrity testing that takes in three factors: (1) the potential harm posed by a dishonest employee in a particular job, (2) the linkage between the test and the assessment process, and (3) the accuracy and validity of the honesty and integrity test. We conclude with implications for practice and future research.     

A Business Model Innovation Typology

An effective business model is the core enabler of any company's performance. Business model innovation is not only becoming more and more important due to increasing and globalizing competition, but also an enormous challenge, both theoretically and practically. Although many managers are eager to consider more disruptive changes to their business model, they often do not know how to articulate their existing or desired business model and, even less so, understand the possibilities for innovating it. One of the steps toward developing more theoretical insight and practical guidelines is the identification of types and the development of a typology of business model innovations. Ten retrospective case studies of business model innovations undertaken by two industrial companies provide the empirical basis for this article. We analyzed the characteristics of these innovations as well as their success rates. The findings suggest that there are indeed various business model innovation types, each with its own characteristics and challenges.     

Un/gendering Social Selves: How Nonbinary People Navigate and Experience a Binarily Gendered World

Based on in‐depth interviews, we explore how people who do not identify exclusively or consistently as either women or men (i.e., nonbinary people) navigate a culture that bifurcates people into women or men. Using an interactionist approach, we first analyze how interviewees employ discourse (e.g., names, identity labels, and pronouns) and the body (e.g., expressions, decoration, and transformation) to present themselves as nonbinary, which we conceptualize as ungendering social selves. Second, we examine the emotional benefits (e.g., authenticity, pride, liberation) and burdens (e.g., fear, rejection, exhaustion) of ungendering. Third, we uncover the emotional, social, and structural conditions under which our nonbinary‐identified participants sometimes present themselves as binarily gendered, which we conceptualize as gendering social selves. We conclude with discussing empirical and theoretical contributions.     

Models for the analysis of C-reactive protein in statin trials

Elevation in C-reactive protein (CRP) is an independent risk factor for cardiovascular disease progression and levels are reduced by treatment with statins. However, on-treatment CRP, given baseline CRP and treatment, is not normally distributed and outliers exist even when transformations are applied. Although classical non-parametric tests address some of these issues, they do not enable straightforward inclusion of covariate information. The aims of this study were to produce a model that improved efficiency and accuracy of analysis of CRP data. Estimation of treatment effects and identification of outliers were addressed using controlled trials of rosuvastatin. The robust statistical technique of MM-estimation was used to fit models to data in the presence of outliers and was compared with least-squares estimation. To develop the model, appropriate transformations of the response and baseline variables were selected. The model was used to investigate how on-treatment CRP related to baseline CRP and estimated treatment effects with rosuvastatin. On comparing least-squares and MM-estimation, MM-estimation was superior to least-squares estimation in that parameter estimates were more efficient and outliers were clearly identified. Relative reductions in CRP were higher at higher baseline CRP levels. There was also evidence of a dose-response relationship between CRP reductions from baseline and rosuvastatin. Several large outliers were identified, although there did not appear to be any relationships between the incidence of outliers and treatments. In conclusion, using robust estimation to model CRP data is superior to least-squares estimation and non-parametric tests in terms of efficiency, outlier identification and the ability to include covariate information.