Global Human Resource Metrics

[Excerpt] What is the logic underlying global human resources (HR) measurement in your organization? In your organization, do you measure the contribution of global HR programs to organizational performance? Do you know what is the most competitive employee mix, e.g., proportion of expatriates vs. local employees, for your business units? (How) do you measure the cost and value of the different types of international work performed by your employees? In the globalized economy, organizations increasingly derive value from human resources, or “talent” as we shall also use the term here (Boudreau, Ramstad & Dowling, in press). The strategic importance of the workforce makes decisions about talent critical to organizational success. Informed decisions about talent require a strategic approach to measurement. However, measures alone are not sufficient, for measures without logic can create information overload, and decision quality rests in substantial part on the quality of measurements. An important element of enhanced global competitiveness is a measurement model for talent that articulates the connections between people and success, as well as the context and boundary conditions that affect those connections. This chapter will propose a framework within which existing and potential global HR measures can be organized and understood. The framework reflects the premise that measures exist to support and enhance decisions, and that strategic decisions require a logical connection between decisions about resources, such as talent, and the key organizational outcomes affected by those decisions. Such a framework may provide a useful mental model for both designers and users of HR measures

Information Outlook, April 2005

Volume 9, Issue 4https://scholarworks.sjsu.edu/sla_io_2005/1003/thumbnail.jp

Information Outlook, March 2007

Volume 11, Issue 3https://scholarworks.sjsu.edu/sla_io_2007/1002/thumbnail.jp

R&D Projects Fostering Small Firms’ Market-Sensing and Customer-Linking Capabilities: A Multivariate Statistics Approach

A large number of empirical studies have recently explored the processes and the conditions under agri-food companies acquire and develop market orientation (e.g. Martin et al. 2009), entrepreneurship (e.g. Holster 2008) and innovation (e.g. Verhees 2005), which have been proven to have a positive relationship with their performance (e.g. Micheels and Gow 2008). A much smaller number of studies focused on how agri-food firms can acquire the capabilities that are necessary to become market-oriented and innovative (e.g. Anderson & Narus 2007), specifically market sensing and customer linking (Day 1994). As a number of public-private partnership projects are attempting to enhance agri-food companies' market orientation and innovation, it is useful to identify which research and dissemination methods effectively develop these capabilities and under which conditions. To attempt to start filling this gap, this study analyses under which conditions public-private projects based on research and dissemination manage to foster market-sensing and customer-linking capabilities of small agri-food firms. Fostering these capabilities in small firms is particularly challenging, as they have limited resources to absorb the new information, learn and apply strategic changes as a result of the learning process. The case of five knowledge-building Seafood Cooperative Research Centre projects based on supply chain mapping and benchmarking methods with the oyster, wild prawn, farmed prawn and finfish industries provides the instrumental cases to the study. We collected data both quantitatively and qualitatively to gain more insight on the cause-effect relationship among variables (Eisenhardt 1989). Then, we analysed data with a structural equation model, whose multivariate statistic approach allows a rigorous analysis of the relationships between latent variables such as market-sensing and customer-linking capabilities and attitudes. Preliminary results can be summarized as follows. First, an estimation of profit margins that different customers make along the chain and an assessment of customers' needs, when customers' concentration and rivalry along the chain is low, are crucial to foster small farms' capabilities. Second, informal networks play a key role for fostering these capabilities from few small firms to the majority of the target.Agribusiness, Marketing,

Information Outlook, April 2007

Volume 11, Issue 4https://scholarworks.sjsu.edu/sla_io_2007/1003/thumbnail.jp

Fuzzy Dynamic Discrimination Algorithms for Distributed Knowledge Management Systems

A reduction of the algorithmic complexity of the fuzzy inference engine has the following property: the inputs (the fuzzy rules and the fuzzy facts) can be divided in two parts, one being relatively constant for a long a time (the fuzzy rule or the knowledge model) when it is compared to the second part (the fuzzy facts) for every inference cycle. The occurrence of certain transformations over the constant part makes sense, in order to decrease the solution procurement time, in the case that the second part varies, but it is known at certain moments in time. The transformations attained in advance are called pre-processing or knowledge compilation. The use of variables in a Business Rule Management System knowledge representation allows factorising knowledge, like in classical knowledge based systems. The language of the first-degree predicates facilitates the formulation of complex knowledge in a rigorous way, imposing appropriate reasoning techniques. It is, thus, necessary to define the description method of fuzzy knowledge, to justify the knowledge exploiting efficiency when the compiling technique is used, to present the inference engine and highlight the functional features of the pattern matching and the state space processes. This paper presents the main results of our project PR356 for designing a compiler for fuzzy knowledge, like Rete compiler, that comprises two main components: a static fuzzy discrimination structure (Fuzzy Unification Tree) and the Fuzzy Variables Linking Network. There are also presented the features of the elementary pattern matching process that is based on the compiled structure of fuzzy knowledge. We developed fuzzy discrimination algorithms for Distributed Knowledge Management Systems (DKMSs). The implementations have been elaborated in a prototype system FRCOM (Fuzzy Rule COMpiler).Fuzzy Unification Tree, Dynamic Discrimination of Fuzzy Sets, DKMS, FRCOM