Critical Race Theory: Focusing the Lens on Human Resource Development

The challenge that continues to face HRD is how to integrate real concerns for diversity into programs, practices, and research. Critical race theory was used as a lens to examine work on diversity published in Human Resource Development Quarterly (HRDQ). Eight publications were selected and analyzed.

Human Resource Development Practices in U.S. Enterprises in Russia: A Literature Review

This study investigated human resource development (HRD) in Russian enterprises, U.S. firms in Russia, or U.S.-Russian joint ventures. Thirty-three articles were selected through a database search and examined using content analysis. Emerging themes included workers’ knowledge and skills, training practices, organizational involvement, responsibility, and communication, and leadership styles.

Queering Human Resource Development

Societal norms for gender and sexual identity and practices tend to be heteronormative or homonormative, often privileging individuals belonging to the normative population. Viewing human resource development through a queer lens can suggest ways to support work environments that welcome all identities.

Cultural Competence in Health Care: Implications for Human Resource Development

This paper reviews the need for cultural competence in health care, the barriers faced by health care professionals as they attempt to deliver culturally competent care, and the implications for human resource development initiatives.

A Human Resource Development Performance Improvement Model for Workers with Mental Retardation in Supported Employment

This literature review discusses the factors for successful job retention of adult workers with mental retardation, including external factors related to work environments and internal issues of the individual worker. Through the synthesis of the literature, a performance improvement model for supported employment is discussed based on Holton’s (1999) human resource development/performance improvement model.

Living on both sides of the fence: A phenomenological study of human resource development professionals as downsizing survivors and strategic human resource development facilitators

This phenomenological study explored how HR professionals who identified themselves as facilitators of strategic HRD (SHRD) perceived the experience of being an organizational agent-downsizing survivor. Criterion and snowball sampling were used to recruit 15 participants for this study. A semi-structured interview guide was used to interview participants. Creswell's (2007) simplified version of Moustakas's (1994) Modification of the Stevick-Colaizzi-Keen Method of Analysis of Phenomenological Data was used to analyze the data. Four main themes and corresponding sub-themes emerged from an inductive data analysis. The four main themes were a) the emotionality of downsizing, b) feeling responsible, c) choice and control, and d) possibilities for growth. Participants perceived downsizing as an emotional organizational change event that required them to manage their own emotions while helping others do the same. They performed their roles within an organizational atmosphere that was perceived as chaotic and filled with apprehension, shock, and a sense of ongoing loss, sadness and grieving. They sometimes experienced guilt and doubt and felt deceptive for having to keep secrets from others when planning for downsizing. Participants felt a strong sense of responsibility to protect employees emotionally, balance employee and organizational interests, and try to ensure the best outcomes for both. Often being there for others meant that they put on their games faces and took care of themselves last. Participants spoke of the importance of choosing one's attitude, being proactive rather than reactive, and finding ways to regain control in the midst of organizational crisis. They also perceived that although downsizing was emotionally difficult to go through that it provided possibilities for self, employee, and organizational growth.

Strategic Human Resources Management Performance Metrics for Unit-Level Managers: An Exploratory Study of U.S Casual Restaurants

Many restaurant organizations have committed a substantial amount of effort to studying the relationship between a firm’s performance and its effort to develop an effective human resources management reward-and-retention system. These studies have produced various metrics for determining the efficacy of restaurant management and human resources management systems. This paper explores the best metrics to use when calculating the overall unit performance of casual restaurant managers. These metrics were identified through an exploratory qualitative case study method that included interviews with executives and a Delphi study. Experts proposed several diverse metrics for measuring management value and performance. These factors seem to represent all stakeholders’interest.

Impact of Employee Management on Hospitality Innovation Success

The author report on a survey of 185 hospitality manager to examine which employee management practices are associated with success in hospitality innovations. The result suggest that successful new hospitality projects are guided by a strategic human resource management approach, have higher level of training, implement behavior- bared evaluation of their front-line staff and empower their employees.

Chinese and British Hotels: Cultural Differences and Management

Rapid tourism development in China has led to an influx of hotels invested in and operated by multi-national hotel companies. The authors examine the impact of cultural differences on employee behavior in China and UK hotels and offer recommendations for expatriate hotel managers to effectively develop human resource management styles while operating properties in China.

Improving resource management in virtualized data centers using application performance models

The rapid growth of virtualized data centers and cloud hosting services is making the management of physical resources such as CPU, memory, and I/O bandwidth in data center servers increasingly important. Server management now involves dealing with multiple dissimilar applications with varying Service-Level-Agreements (SLAs) and multiple resource dimensions. The multiplicity and diversity of resources and applications are rendering administrative tasks more complex and challenging. This thesis aimed to develop a framework and techniques that would help substantially reduce data center management complexity.^ We specifically addressed two crucial data center operations. First, we precisely estimated capacity requirements of client virtual machines (VMs) while renting server space in cloud environment. Second, we proposed a systematic process to efficiently allocate physical resources to hosted VMs in a data center. To realize these dual objectives, accurately capturing the effects of resource allocations on application performance is vital. The benefits of accurate application performance modeling are multifold. Cloud users can size their VMs appropriately and pay only for the resources that they need; service providers can also offer a new charging model based on the VMs performance instead of their configured sizes. As a result, clients will pay exactly for the performance they are actually experiencing; on the other hand, administrators will be able to maximize their total revenue by utilizing application performance models and SLAs. ^ This thesis made the following contributions. First, we identified resource control parameters crucial for distributing physical resources and characterizing contention for virtualized applications in a shared hosting environment. Second, we explored several modeling techniques and confirmed the suitability of two machine learning tools, Artificial Neural Network and Support Vector Machine, to accurately model the performance of virtualized applications. Moreover, we suggested and evaluated modeling optimizations necessary to improve prediction accuracy when using these modeling tools. Third, we presented an approach to optimal VM sizing by employing the performance models we created. Finally, we proposed a revenue-driven resource allocation algorithm which maximizes the SLA-generated revenue for a data center.^

Fair and QoS-oriented resource management in heterogeneous networks

In this paper, a heterogeneous network composed of femtocells deployed within a macrocell network is considered, and a quality-of-service (QoS)-oriented fairness metric which captures important characteristics of tiered network architectures is proposed. Using homogeneous Poisson processes, the sum capacities in such networks are expressed in closed form for co-channel, dedicated channel, and hybrid resource allocation methods. Then a resource splitting strategy that simultaneously considers capacity maximization, fairness constraints, and QoS constraints is proposed. Detailed computer simulations utilizing 3GPP simulation assumptions show that a hybrid allocation strategy with a well-designed resource split ratio enjoys the best cell-edge user performance, with minimal degradation in the sum throughput of macrocell users when compared with that of co-channel operation.

Fuzzy Modeling and Control Based Virtual Machine Resource Management

Virtual machines (VMs) are powerful platforms for building agile datacenters and emerging cloud systems. However, resource management for a VM-based system is still a challenging task. First, the complexity of application workloads as well as the interference among competing workloads makes it difficult to understand their VMs’ resource demands for meeting their Quality of Service (QoS) targets; Second, the dynamics in the applications and system makes it also difficult to maintain the desired QoS target while the environment changes; Third, the transparency of virtualization presents a hurdle for guest-layer application and host-layer VM scheduler to cooperate and improve application QoS and system efficiency. This dissertation proposes to address the above challenges through fuzzy modeling and control theory based VM resource management. First, a fuzzy-logic-based nonlinear modeling approach is proposed to accurately capture a VM’s complex demands of multiple types of resources automatically online based on the observed workload and resource usages. Second, to enable fast adaption for resource management, the fuzzy modeling approach is integrated with a predictive-control-based controller to form a new Fuzzy Modeling Predictive Control (FMPC) approach which can quickly track the applications’ QoS targets and optimize the resource allocations under dynamic changes in the system. Finally, to address the limitations of black-box-based resource management solutions, a cross-layer optimization approach is proposed to enable cooperation between a VM’s host and guest layers and further improve the application QoS and resource usage efficiency. The above proposed approaches are prototyped and evaluated on a Xen-based virtualized system and evaluated with representative benchmarks including TPC-H, RUBiS, and TerraFly. The results demonstrate that the fuzzy-modeling-based approach improves the accuracy in resource prediction by up to 31.4% compared to conventional regression approaches. The FMPC approach substantially outperforms the traditional linear-model-based predictive control approach in meeting application QoS targets for an oversubscribed system. It is able to manage dynamic VM resource allocations and migrations for over 100 concurrent VMs across multiple hosts with good efficiency. Finally, the cross-layer optimization approach further improves the performance of a virtualized application by up to 40% when the resources are contended by dynamic workloads.

Land Use and Climate Change Impacts on the Hydrology of the Upper Mara River Basin, Kenya: Results of a Modeling Study to Support Better Resource Management

Some of the most valued natural and cultural landscapes on Earth lie in river basins that are poorly gauged and have incomplete historical climate and runoff records. The Mara River Basin of East Africa is such a basin. It hosts the internationally renowned Mara-Serengeti landscape as well as a rich mixture of indigenous cultures. The Mara River is the sole source of surface water to the landscape during the dry season and periods of drought. During recent years, the flow of the Mara River has become increasingly erratic, especially in the upper reaches, and resource managers are hampered by a lack of understanding of the relative influence of different sources of flow alteration. Uncertainties about the impacts of future climate change compound the challenges. We applied the Soil Water Assessment Tool (SWAT) to investigate the response of the headwater hydrology of the Mara River to scenarios of continued land use change and projected climate change. Under the data-scarce conditions of the basin, model performance was improved using satellite-based estimated rainfall data, which may also improve the usefulness of runoff models in other parts of East Africa. The results of the analysis indicate that any further conversion of forests to agriculture and grassland in the basin headwaters is likely to reduce dry season flows and increase peak flows, leading to greater water scarcity at critical times of the year and exacerbating erosion on hillslopes. Most climate change projections for the region call for modest and seasonally variable increases in precipitation (5–10 %) accompanied by increases in temperature (2.5–3.5 °C). Simulated runoff responses to climate change scenarios were non-linear and suggest the basin is highly vulnerable under low (−3 %) and high (+25 %) extremes of projected precipitation changes, but under median projections (+7 %) there is little impact on annual water yields or mean discharge. Modest increases in precipitation are partitioned largely to increased evapotranspiration. Overall, model results support the existing efforts of Mara water resource managers to protect headwater forests and indicate that additional emphasis should be placed on improving land management practices that enhance infiltration and aquifer recharge as part of a wider program of climate change adaptation.

The 6P's of sales resource management

This paper will explore a data-driven approach called Sales Resource Management (SRM) that can provide real insight into sales management. The DSMT (Diagnosis, Strategy, Metrics and Tools) framework can be used to solve field sales management challenges. This paper focus on the 6P's strategy of SRM and illustrates how to use them to solve the CAPS (Concentration, Attrition, Performance and Spend) challenges. © 2010 IEEE.

Sales resource management training:a guide to developing effective salespeople

This paper adopts a sales resource management (SRM) framework to provide guidance on how to develop effective salespeople via sales training. SRM can be used to identify the individual training needs based on the individual-based modelling data. The individual-based modelling data can also be used to evaluate the outcome of sales training. This paper also gives some suggestions on the forms of sales training which are most likely to develop effective salespeople. © 2010 IEEE.