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High-involvement work processes (HIWPs) are associated with high levels of employee influence over the work process, such as high levels of control over how to handle individual job tasks or a high level of involvement at team or workplace level in designing work procedures. When implementations of HIWPs are accompanied by companion investments in human capital – for example, in better information and training, higher pay and stronger employee voice – it is appropriate to talk not only of HIWPs but of “high-involvement work systems” (HIWSs). This chapter reviews the theory and practice of HIWPs and HIWSs. Across a range of academic perspectives and societies, it has regularly been argued that steps to enhance employee involvement in decision-making create better opportunities to perform, better utilization of skill and human potential, and better employee motivation, leading, in turn, to various improvements in organizational and employee outcomes.

However, there are also costs to increased employee involvement and the authors review the important economic and sociopolitical contingencies that help to explain the incidence or distribution of HIWPs and HIWSs. The authors also review the research on the outcomes of higher employee involvement for firms and workers, discuss the quality of the research methods used, and consider the tensions with which the model is associated. This chapter concludes with an outline of the research agenda, envisaging an ongoing role for both quantitative and qualitative studies. Without ignoring the difficulties involved, the authors argue, from the societal perspective, that the high-involvement pathway should be considered one of the most important vectors available to improve the quality of work and employee well-being.

Post-fordist production systems emphasize the need to tap workers’ knowledge to enhance productivity and quality. Often overlooked, however, is the potential conflict in expecting workers to contribute to processes that may make their jobs harder. This article compares employee participation schemes at two General Motors assembly plants to illustrate the potency of this dilemma and the range of ways managers focus or limit employee participation to achieve the company's goals. In Silao, Mexico, General Motors carefully constructed a labor relations environment that cultivated broad employee participation. In Janesville, Wisconsin, local managers placed constraints on employee participation to ensure continuous production.

Purpose – Addresses labor control in fields where familiar organizational and occupational controls are weak, notably postindustrial arenas characterized by networks, heterogeneity, and change.Methodology/approach – Proposes that labor control operates via socio-technical networks composed of diverse ties to social actors, technologies, and typifications. Data from an interview-based study of early website production work is used to examine the impact of such a network.Findings – Socio-technical networks constrained web workers#x02019; actions but also offered opportunities for autonomous discretion. Some shifting between networked and hierarchical controls occurred in larger organizations.Research implications/limitations – The role of networks in the labor process is not well understood; this study provides a starting point.Social implications – Socio-technical networks are heterogeneous and lack common status metrics, making inequality among workers difficult to gauge and address. Further, since networked controls are decentralized, their pressures are not easily identified or resisted by workers.Originality/value – This chapter describes a form of labor control that may characterize some postindustrial fields more closely than traditional models. In addition, it contributes new insights on how work is shaped by technical networks and abstract typifications.

This chapter studies the consequences of firm delayering on wages and the wage distribution inside firms. I consider a market-based tournament model with asymmetric information to endogenize firms’ delayering decisions. My model predicts that when the CEO becomes more productive, firms grow in size. When the CEO becomes sufficiently productive, firms delayer. After delayering, wages at all levels rise and the wage gap between the CEO and the laborers widens. These predictions capture the dynamic process of firms’ structure and size changes and match a set of empirical findings in recent studies that are not well explained by existing theories.

The study analyses production worker hiring standards based on time series personnel records drawn from matched plants in the U.S., U.K., Italy, the Netherlands and Belgium. Our hypothesis is that labor market regulation pushes upwards hiring standards for production workers. Labor market regulation is measured both by an employment protection index, and by workforce average tenure as a proxy for insider power. We find that the average tenure variable gives more robust results than the index. Its effect is to increase education standards, but to reduce starting age standards. The expected positive effect of employment protection on hiring standards is found in simple regressions, but is not generally supported by the multivariate analysis once other influences are held constant. However, union density is found to increase hiring standards, and might take over the effect of employment protection as an indicator of overall regulatory pressure. We also find a strong substitutability between recruits’ prior experience and education. This substitutability indicates the power of education to widen job opportunities for inexperienced workers.

Many recent chapters have investigated whether data from internet search engines such as Google can help improve nowcasts or short-term forecasts of macroeconomic variables. These chapters construct variables based on Google searches and use them as explanatory variables in regression models. We add to this literature by nowcasting using dynamic model selection (DMS) methods which allow for model switching between time-varying parameter regression models. This is potentially useful in an environment of coefficient instability and over-parameterization which can arise when forecasting with Google variables. We extend the DMS methodology by allowing for the model switching to be controlled by the Google variables through what we call “Google probabilities”: instead of using Google variables as regressors, we allow them to determine which nowcasting model should be used at each point in time. In an empirical exercise involving nine major monthly US macroeconomic variables, we find DMS methods to provide large improvements in nowcasting. Our use of Google model probabilities within DMS often performs better than conventional DMS methods.

In this chapter we study job design. Do organizations plan precisely how the job is to be done ex ante, or ask workers to determine the process as they go? We first model this decision and predict complementarity among these following job attributes: multitasking, discretion, skills, and interdependence of tasks. We argue that characteristics of the firm and industry (e.g., product and technology, organizational change) can explain observed patterns and trends in job design. We then use novel data on these job attributes to examine these issues. As predicted, job designs tend to be “coherent” across these attributes within the same job. Job designs also tend to follow similar patterns across jobs in the same firm, and especially in the same establishment: when one job is optimized ex ante, others are more likely to be also. There is evidence that firms segregate different types of job designs across different establishments. At the industry level, both computer usage and R&D spending are related to job design decisions.

This chapter examines and discusses the unintended outcomes of the production bonus scheme the mine had instituted to motivate and increase the productivity of the frontline mining teams. This is crucial given that the maladministration of the bonus system could lead to a range of undesired outcomes such as deteriorating levels of trust between management and frontline workers, prioritisation of production at the expense of safety, poor work relations and ultimately low levels of organisational, employee and team performance. There are a number of organisational, management and labour factors that can render a production bonus scheme effective or ineffective. These factors influence the nature and extent of worker reactions to the bonus scheme.

This chapter examines and discusses the factors that influenced the reaction of the mining teams to the team-based production bonus scheme and the extent to which mine management fulfilled its side of the bargain in the implementation of the production bonus. The chapter highlights the manner in which the team-based bonus system influenced teams of stope workers to engage in their informal organisational practice of making plan (planisa) in order to offset the snags that jeopardised their prospects of earning the production bonus. The chapter reveals that, to a large extent, the productivity bonus generated conflict rather than cooperation at the point of production down the mine. As a result, the incentive scheme failed to live up to expectations by not eliciting the desired levels of organisational, worker and team performance at the rock-face.