The social science and business literatures on procedural justice or fair process attest that improvements in procedural fairness can be expected to improve both a firm's performance and the commitment and trust of the individuals involved with it. This article examines the relevance of procedural justice for family business. When a family is an influential component of a particular business system, the application of justice is typically rendered more complex than might be the case for nonfamily firms. Different criteria (need, merit, and equality) guide the application of distributive justice among families, firms, and shareholders. This divergence in criterion also lies at the heart of many conflicts inside the family business. In this article, we argue that the application of procedural justice reduces occurrences of conflict and, in some cases, may eliminate conflict altogether. We propose a definition of fair process that extends and enriches the one existing in the literature. We offer five fundamental criteria essential to the effectiveness of fair process in family firms. We conclude with a series of case studies that illustrate typical questions faced inside family businesses. We show that a lack of fairness in the decision and managerial processes governing these businesses and their associated families is a source of conflict. We describe how increasing fair process practices improves the performance of these businesses while also increasing the satisfaction of those associated with them.

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This paper discusses perturbation bounds for the stationary distribution of a finite indecomposable Markov chain. Existing bounds are reviewed. New bounds are presented which more completely exploit the stochastic features of the perturbation and which also are easily computable. Examples illustrate the tightness of the bounds and their application to bounding the error in the Simon–Ando aggregation technique for approximating the stationary distribution of a nearly completely decomposable Markov chain.

This paper deals with the nonlinear complementarity problem on the product space of unit simplices, S. A simplicial variable dimension algorithm developed by van der Laan and Talman for proper labellings of S is extended to the case of general labellings. General labellings allow a more natural description of the complementarity problem on the boundary of S. A distinctive feature of the new algorithm is that lower dimensional simplicial movement can occur both on the boundary and in the interior of S. In contrast, the van der Laan and Talman algorithm for proper labellings of S allows lower dimensional simplicial movement only in the interior of S. Computational experiments confirm the usefulness of general labellings for solving nonlinear complementarity problems.

Fair process research has shown that people care not only about outcomes, but also about the process that produces these outcomes. For a decision process to be seen as fair, the people affected must have the opportunity to give input and possibly to influence the decision, and the decision process and rationale must be transparent and clear. Existing research has shown empirically that fair process enhances both employee motivation and performance in execution. However, work to date has not addressed why fair process is so often violated in practice. This paper breaks new ground by analytically examining the subtle trade-offs involved. We develop a model of fair process in a principal-agent (i.e., manager-employee) context, rooted in psychological preferences for autonomy and fairness. We show that indeed fair process will not always be used, and why the hoped-for benefits may be insufficient to convince management to use fair process.fair process, engagement, transparency, social preference, agency theory, motivation