Knowledge Management in Projects

Knowledge transfer in project-based organizations has been recognized as a productivity and economic growth promoter that is currently not properly managed. Therefore, it is critical to investigate the importance of knowledge regarding its acquisition, use, and transfer across all departments of an organization. The main objective of this work was to evaluate how modern organizations from different business sectors manage and share their knowledge by following the knowledge management and transfer life cycles when developing their projects. Organizational surveys were performed to project managers working in project management within in multiple Portuguese industries (or with representation in Portugal) from multiple business sectors and holding different hierarchical positions. The present study revealed that most companies can identify, capture, and retain the relevant knowledge, and apply it to the development of other projects. Thus, the importance of implementing the knowledge management transfer to other projects is crucial for successful project implementation and management.

Writing Organizational Survey Items That Predict What Matters in Organizations

Organizational surveys are essential tools for gathering data in 21st-century organizations. This chapter provides a practitioner-oriented guide to developing survey content and items. In this chapter, the authors highlight the need to first determine highly specific survey project goals and deduce broad survey content domains based on those goals. They advise practitioners to build close relationships with employees and line managers to develop short survey tools that are organizationally relevant and predictive of organizational outcomes of interest (i.e., customer satisfaction, employee retention). Based on extant research evidence around item and scale development, they discuss response formats, survey length considerations, respondent literacy issues, and cultural as well as language considerations relevant to survey development. They conclude with ethical considerations and a brief summary.

Linkage Analysis

Linkage analysis is a framework for determining the impact that employee attitudes, as measured by organizational surveys, have on business outcomes. Linking employee attitudes to outcomes such as employee turnover and performance provides a compelling business case for executives to invest both emotionally and financially in employee surveys. The current chapter reviews the large body of research supporting the linkage analysis framework, as well as common approaches and challenges. Three case studies from the field are also presented, along with practical recommendations for translating linkage results into meaningful actions that organizations can take. The authors conclude by sharing the implications of linkage analysis in an era of big data.

Explaining Diversity Management Outcomes

This chapter is focused on what survey research has recently contributed to our knowledge of diversity management outcomes and what is to be done to bring this field further. To this end it provides an overview of recent survey research articles on diversity management outcomes. They reveal inconsistent results: whereas organizational surveys have yielded inconclusive findings with regard to the outcomes of diversity policies and management, employee surveys have often shown positive relationships between diversity policies and management and employee outcomes. The inconsistency of these findings are then further explained by discussing four main methodological weaknesses of current survey research on diversity management outcomes and by relating these issues to the main gaps in our knowledge. A research agenda for future survey research on diversity management outcomes is outlined to which questions regarding when and why diversity management would lead to favourable outcomes are central.

Considering the Effects of Nonresponse on Correlations Between Surveyed Variables

Nonresponse to organizational surveys is a vexing problem. Although scholars have a reasonable understanding of how systematic nonresponse influences estimates of population means, they are likely to have less context for understanding how it impacts correlations. We present the results of a simulation study designed to provide a frame of reference for understanding the extent to which systematic nonresponse can bias observed correlations between surveyed variables. Although there were cases where notable levels of bias were observed in the correlations, the majority of situations simulated resulted in mean observed correlations that were within .05 of the true correlation. The variety of situations simulated allows researchers to evaluate the external validity of observed correlations in their data – a critical goal for survey researchers in practice and academics.