Attributions and the Evaluation of Dynamic Performance

As research into the dynamic characteristics of job performance across time has continued to accumulate, associated implications for performance appraisal have become evident. At present, several studies have demonstrated that systematic trends in job performance across time influence how performance is ultimately judged. However, little research has considered the processes by which the performance trend-performance rating relationship occurs. In the present study, I addressed this gap. Specifically, drawing on attribution theory, I proposed and tested a model whereby the performance trend-performance rating relationship occurs through attributions to ability and effort. The results of this study indicated that attributions to ability, but not effort, mediate the relationship between performance trend and performance ratings and that this relationship depends on attribution-related cues. Implications for performance appraisal research and theory are discussed.

A decision support framework for infrastructure maintenance investment decision-making

Infrastructure management agencies are facing multiple challenges, including aging infrastructure, reduction in capacity of existing infrastructure, and availability of limited funds. Therefore, decision makers are required to think innovatively and develop inventive ways of using available funds. Maintenance investment decisions are generally made based on physical condition only. It is important to understand that spending money on public infrastructure is synonymous with spending money on people themselves. This also requires consideration of decision parameters, in addition to physical condition, such as strategic importance, socioeconomic contribution and infrastructure utilization. Consideration of multiple decision parameters for infrastructure maintenance investments can be beneficial in case of limited funding. Given this motivation, this dissertation presents a prototype decision support framework to evaluate trade-off, among competing infrastructures, that are candidates for infrastructure maintenance, repair and rehabilitation investments. Decision parameters' performances measured through various factors are combined to determine the integrated state of an infrastructure using Multi-Attribute Utility Theory (MAUT). The integrated state, cost and benefit estimates of probable maintenance actions are utilized alongside expert opinion to develop transition probability and reward matrices for each probable maintenance action for a particular candidate infrastructure. These matrices are then used as an input to the Markov Decision Process (MDP) for the finite-stage dynamic programming model to perform project (candidate)-level analysis to determine optimized maintenance strategies based on reward maximization. The outcomes of project (candidate)-level analysis are then utilized to perform network-level analysis taking the portfolio management approach to determine a suitable portfolio under budgetary constraints. The major decision support outcomes of the prototype framework include performance trend curves, decision logic maps, and a network-level maintenance investment plan for the upcoming years. The framework has been implemented with a set of bridges considered as a network with the assistance of the Pima County DOT, AZ. It is expected that the concept of this prototype framework can help infrastructure management agencies better manage their available funds for maintenance.

Ecological genetics of Melaleuca quinquenervia (Myrtaceae): Population variation in Florida and its influence on performance of the biological control agent Oxyops vitiosa (Coleoptera: Curculionidae)

Melaleuca quinquenervia (Cav.) Blake (Myrtaceae) was imported into Florida from Australia over a century ago as a landscape plant. A favorable climate and periodic wildfires helped M. quinquenervia thrive; it now occupies about 200,000 hectares in southern Florida. A biological control (i.e., biocontrol) program against M. quinquenervia has been initiated, but not all biocontrol releases are successful. Some scientists have argued that poor biocontrol agent success may relate to genetic differences among populations of invasive weeds. I tested this premise by determining (1) the number and origins of M. quinquenervia introductions into Florida, (2) whether multiple introduction events resulted in the partitioning of Florida's M. quinquenervia populations into discrete biotypes, and (3) whether Oxyops vitiosa, an Australia snout beetle imported to control this weed, might discriminate among putative M. quinquenervia biotypes. Careful scrutiny of early horticultural catalogs and USDA plant introduction records suggested at least six distinct introduction events. Allozyme analyses indicated that the pattern of these introductions, and the subsequent redistribution of progeny, has resulted in geographic structuring of the populations in southern Florida. For example, trees on Florida's Gulf Coast had a greater effective number of alleles and exhibited greater heterozygosity than trees on the Atlantic Coast. Essential oil yields from M. quinquenervia leaves followed a similar trend; Gulf Coast trees yielded nearly twice as much oil as Atlantic Coast trees when both were grown in a common garden. These differences were partially explained by the predominance of a chemical phenotype (chemotype) very rich in the sesquiterpene (E)-nerolidol in M. quinquenervia trees from the Gulf Coast, but rich in a mixture of the monoterpene 1,8-cineole and the sesquiterpene viridiflorol in trees from the Atlantic Coast. Performance of O. vitiosa differed dramatically in laboratory studies depending on the chemotype of the foliage they were fed. Larval survivorship was four-fold greater on the (E)-nerolidol chemotype. Growth was also greater, with adult O. vitiosa gaining nearly 50% more biomass on the (E)-nerolidol plants than on the second chemotype. The results of this study thus confirmed the premise that plant genotype can affect the population dynamics of insects released as weed biocontrols. ^

Control of MNC environmental performance and the challenges of subsidiary network dimensions

The trend of green consumerism and increased standardization of environmental regulations has driven multinational corporations (MNCs) to seek standardization of environmental practices or at least seek to be associated with such behavior. In fact, many firms are seeking to free ride on this global green movement, without having the actual ecological footprint to substantiate their environmental claims. While scholars have articulated the benefits from such optimization of uniform global green operations, the challenges for MNCs to control and implement such operations are understudied. For firms to translate environmental commitment to actual performance, the obstacles are substantial, particularly for the MNC. This is attributed to headquarters' (HQ) control challenges (1) in managing core elements of the corporate environmental management (CEM) process and specifically matching verbal commitment and policy with ecological performance and by (2) the fact that the MNC operates in multiple markets and the HQ is required to implement policy across complex subsidiary networks consisting of diverse and distant units. Drawing from the literature on HQ challenges of MNC management and control, this study examines (1) how core components of the CEM process impact optimization of global environmental performance (GEP) and then uses network theory to examine how (2) a subsidiary network's dimensions can present challenges to the implementation of green management policies. It presents a framework for CEM which includes (1) MNCs' Verbal environmental commitment, (2) green policy Management which guides standards for operations, (3) actual environmental Performance reflected in a firm's ecological footprint and (4) corporate environmental Reputation (VMPR). Then it explains how an MNC's key subsidiary network dimensions (density, diversity, and dispersion) create challenges that hinder the relationship between green policy management and actual environmental performance. It combines content analysis, multiple regression, and post-hoc hierarchal cluster analysis to study US manufacturing MNCs. The findings support a positive significant effect of verbal environmental commitment and green policy management on actual global environmental performance and environmental reputation, as well as a direct impact of verbal environmental commitment on green policy management. Unexpectedly, network dimensions were not found to moderate the relationship between green management policy and GEP.