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Drawing on broaden-and-build theory and promotion- and prevention-focus theory, the authors examined the role of positive and negative affectivity (PANA) on the riskiness of investment decisions. The authors also examined the mediating impact of financial knowledge network intensity (i.e. the level of communication with financially literate others in employees' social network) on the PANA—riskiness of investment decisions relationship.

Study 1 used a sample of undergraduate students and operationalized risk using a hypothetical investment scenario. Study 2 replicated and extended the Study 1 findings using employees and operationalized risk using their real-world investment allocations.

Both Studies 1 and 2 provided support for the negative direct relationship between NA and the riskiness of investment decisions. Study 2 found PA was marginally positively related to the riskiness of investment decisions. Financial knowledge network intensity mediated the relationship between NA and the riskiness of investment decisions in Study 2.

The findings suggest that employees who see the world in a generally negative light tended to have weaker financial knowledge networks, and this may be one mechanism that explains why they make low-risk investments.

Financial knowledge networks can provide access to critical information regarding investment opportunities. Socialization training or social mixers can be used to help employees build and improve their financial knowledge networks.

The authors integrate the research on PANA, social networks, and investment decisions to illuminate the social network processes that explain how affectivity impacts the riskiness of retirement investment decisions.

A distinguishing feature of strategic human resource management research is an emphasis on human resource (HR) systems, rather than individual HR practices as a driver of individual and organizational performance. Yet, there remains a lack of agreement regarding what these systems are, which practices comprise these systems, how these systems operate, and how they should be studied. Our goal in this paper is to take a step toward identifying and addressing several conceptual and methodological issues regarding HR systems. Conceptually, we argue that HR systems should be targeted toward some strategic objective and operate by influencing (1) employee knowledge, skills, and abilities, (2) employee motivation and effort, and (3) opportunities for employees to contribute. Methodologically, we explore issues related to the relationships among policies and practices, sampling issues, identifying the appropriate referent group(s), and who should serve as key informants for HR system studies.

Bradley J. Alge is an associate professor of Management at Purdue University's Krannert School of Management. He received his Ph.D. in business administration from The Ohio State University, and an MBA from Kent State University. Professor Alge received his BBA from the University of Notre Dame, where he majored in MIS and was also a member of the 1988 Division I NCAA National Championship football team. Prior to entering academia, he worked as a consultant for Accenture. Professor Alge studies issues of human–technology interaction (e.g., electronic monitoring, virtual teams) and the effects of technology on individual and group attitudes and behaviors on the job. He has published in leading management and psychology journals including the Journal of Applied Psychology, Personnel Psychology, and Organizational Behavior and Human Decision Processes.

Blockmodeling is viewed often as a data reduction method. However, this is a simplistic view of the class of methods designed to uncover social structures, identify subgroups, and reveal emergent roles. Worse, this view misses the richness of the method as a tool for uncovering novel human resource management (HRM) insights. Here, we provide a brief overview of some essentials of blockmodeling and discuss research questions that can be addressed using this approach in applied HRM settings. Finally, we offer an empirical example to illustrate blockmodeling and the types of information that can be gleaned from its implementation.