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The author develops a bilateral Nash bargaining model under value uncertainty and private/asymmetric information, combining ideas from axiomatic and strategic bargaining theory. The solution to the model leads organically to a two-tier stochastic frontier (2TSF) setup with intra-error dependence. The author presents two different statistical specifications to estimate the model, one that accounts for regressor endogeneity using copulas, the other able to identify separately the bargaining power from the private information effects at the individual level. An empirical application using a matched employer–employee data set (MEEDS) from Zambia and a second using another one from Ghana showcase the applied potential of the approach.

On the basis of labor economics theories, this study examines how adjustment in human capital accounts for labor cost stickiness.

This study makes use of employee education level as a measure of the quality of human capital and relies on data from Chinese public firms to conduct the empirical test. This study focuses on two important components of labor cost changes: one corresponding to the adjustment in the number of employees (capacity adjustment) and another corresponding to the adjustment in the mix of employee education levels (quality adjustment).

This study reveals that labor cost changes driven by the adjustment of employee education level are sticky. This stickiness cannot be explained by the standard adjustment cost theory. This further shows that firms that actively adjust their employee quality during downturns experience improved future performance. The findings are robust to alternative measures and specifications.

This study provides new evidence for and insights into the cost behavior literature. Previous studies treat input resources in a homogenous way and focus on the effect of capacity adjustment. This study considers the heterogeneity of resources and examines three dimensions of salary cost adjustment: capacity, structure, and unit cost. In line with the economic theory of sticky costs proposed by Banker et al. (2013a), the study’s evidence sheds light on the additional underlying economic mechanisms driving cost stickiness behavior. Specifically, managers asymmetrically adjust both employee structure and average salaries, in addition to employee number. This study also adds to the existing knowledge of the consequences of managers' actions regarding cost behavior.

In recent years, there has been a growing interest in the potential of data analytics to enhance project delivery. Yet many argue that its application in projects is still lagging behind other disciplines. This paper aims to provide a review of the current use of data analytics in project delivery encompassing both academic research and practice to accelerate current understanding and use this to formulate questions and goals for future research.

We propose to achieve the research aim through the creation of a systematic review of the status of data analytics in project delivery. Fusing the methodology of integrative literature review with a recently established practice to include both white and grey literature amounts to an approach tailored to the state of the domain. It serves to delineate a research agenda informed by current developments in both academic research and industrial practice.

The literature review reveals a dearth of work in both academic research and practice relating to data analytics in project delivery and characterises this situation as having “more gap than knowledge.” Some work does exist in the application of machine learning to predicting project delivery though this is restricted to disparate, single context studies that do not reach extendible findings on algorithm selection or key predictive characteristics. Grey literature addresses the potential benefits of data analytics in project delivery but in a manner reliant on “thought-experiments” and devoid of empirical examples.

Based on the review we articulate a research agenda to create knowledge fundamental to the effective use of data analytics in project delivery. This is structured around the functional framework devised by this investigation and highlights both organisational and data analytic challenges. Specifically, we express this structure in the form of an “onion-skin” model for conceptual structuring of data analytics in projects. We conclude with a discussion about if and how today’s project studies research community can respond to the totality of these challenges. This paper provides a blueprint for a bridge connecting data analytics and project management.