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This paper aims to compare different univariate forecasting methods to provide a more accurate short-term forecasting model on the crude oil price for rendering a reference to manages.

Six different univariate methods, namely the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey forecast, the hybrid grey model and the seasonal autoregressive integrated moving average (SARIMA), have been used.

The authors found that the grey forecast is a reliable forecasting method for crude oil prices.

The contribution of this research study is using a small size of data and comparing the forecasting results of the six univariate methods. Three commonly used evaluation criteria, mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percent error (MAPE), were adopted to evaluate the model performance. The outcome of this work can help predict the crude oil price.

Using the Analytical Hierarchy Process, this study aims to identify organizational and market factors that prevent hoteliers from adopting greener management systems. This study first constructs a list of critical factors based on expert judgments reported in the literature and the responses from a select group of experienced managers. Then, the list is further refined by six hotel managers who consolidate the factors, which results in four categories explained by 20 items. Subsequently, this study draws from surveys on an expert panel consisting of 20 study subjects who are familiar with hotel operations. The survey results show that hoteliers’ abilities to create a greener image are the most important factor influencing the managers’ decisions to adopt environmentally friendly management schemes. Nevertheless, their motivation of adopting a green strategy seems to be associated with a marketing strategy, instead of generic environmental stewardship.

The ecosystem concept has attracted attention in information system research to explain business competition, innovation and many other emerging phenomena. Existing studies focus more on a single ecosystem type or a single ecosystem goal and pay little attention to the ecosystem’s evolution. The objective of the study is to investigate the factors that impact the evolution of the information ecosystem (IE) to gain a better understanding of strategic thinking.

The IE involves many actors, so the multi-case study approach is conducted with purposeful sampling to recruit all the significant ecosystem actors. The collected qualitative data are analyzed by coding data, exploring data relationships and structuring pattern steps; institutional theory is used as a theoretical framework.

The results demonstrate that industry practices, laws and regulations, new actors and the mimetic pressure of outsourcers drive the growth of the ecosystem. Strategy intention, cost pressure and normative pressure all contribute to the IE’s evolution.

The concept of ecosystems has attracted attention in information system research. The study investigates the factors contributing to the evolution of the IE from an institutional theory perspective. Our suggestion is that new players can find a niche in offering information technology (IT)/ information services (IS)-related solutions to survive in the ecosystem; however, they need to pay attention to the normative pressure.

The purpose of this paper is to identify the critical parameters that influence ball grid array and chip size package fatigue life in a random vibration environment by using a design of experimental (DOE) approach using simulation results.

The use of DOE and analysis of variation to identify the critical parameters and a response surface to generate a functional form for global modeling would be determined. Once the global modeling’s functional form was known, it can be used as boundary condition, which would be input to a local model. Knowing the critical stress, one would estimate the fatigue life from a damage model. It is the curvature of the printed wiring board in the region of the component of interest that is driving the component’s solder joint damage. The approach in this present work involves global-local modeling approaches. In the global model approach, the vibration response of the printed circuit board (PCB) will be determined.

This global model will give the response of the PCB at specific component locations of interest. This response is then fed into a local stress analysis for accurate assessment of the critical stresses in the solder joints of interest. The stresses are then fed into a fatigue damage model to predict the life.

The analysis proposed in this paper uses a failure type approach to damage analysis and involves global and local model approaches.

The purpose of this study is to examine the indirect impact of job demands on recovery self-efficacy via the mediation of job burnout. The study also investigates the moderating effects of school-to-work facilitation and psychological detachment in the indirect relationship between job demands and recovery self-efficacy.

The study recruited and surveyed 263 employed graduate students in the executive master of business administration program in Taiwan. Regression analysis was used to examine the proposed relationships.

The results showed that job burnout mediated the relationship between job demands and recovery self-efficacy. The relationship was weaker when school-to-work facilitation and psychological detachment were high.

This study confirms the indirect effects of job demands on recover self-efficacy through job burnout and provides new insights into the role of school-to-work facilitation and psychological detachment to enhance the recovery in the JD-R model.

This paper aims to develop the thermal resistance network model based on the heat dissipation paths from the multi-die stack to the ambient and takes into account the composite effects of the thermal spreading resistance and one-dimensional (1D) thermal resistance. The thermal spreading resistance comprises majority of the thermal resistance when heat flows in the horizontal direction of a large plate. The present study investigates the role of determining the temperature increase compared to the thermal resistances intrinsic to the 3D technology, including the thermal resistances of bonding layers and through silicon vias (TSVs).

This paper presents an effective method that can be applied to predict the thermal failure of the heat source of silicon chips. An analytical model of the 3D integrated circuit (IC) package, including the full structure, is developed to estimate the temperature of stacked chips. Two fundamental theories are used in this paper – Laplace’s equation and the thermal resistance network – to calculate 1D thermal resistance and thermal spreading resistance on the 3D IC package.

This paper provides a comprehensive model of the 3D IC package, thus improving the existing analytical approach for predicting the temperature of the heat source on the chip for the 3D IC package.

Based on the aforementioned shortcomings, the present study aims to determine if the use of an analytical resistance model would improve the handling of a temperature increase on the silicon chips in a 3D IC package. To achieve this aim, a simple rectangular plate is utilized to analyze the temperature of the heat source when applying the heat flux on the area of the heat source. Next, the analytical model of a pure plate is applied to the 3D IC package, and the temperature increase is analyzed and discussed.

The main contribution of this paper is the use of a simple concept and a theoretical resistance network model to improve the current understanding of thermal failure by redesigning the parameters or materials of a printed circuit board.

In this paper, an analytical model of a 3D IC package was proposed based on the calculation of the thermal resistance and the analysis of the network model.

The aim of this work was to estimate the mean temperature of the silicon chips and understand the heat convection paths in the 3D IC package. The results reveal these phenomena of the complete structure, including TSV and bump, and highlight the different thermal conductivities of the materials used in creating the 3D IC packages.

Human resource management may have great distinctions in different cultural contexts; past researches have increasingly discovered the differences between Eastern and Western perspectives. For employee selection, Chinese bosses usually employ acquaintances and relative bases on accumulated favors and relationship intensity in Chinese society. This study aims to investigate the relative importance of P-J fit, P-O fit and guanxi when Chinese recruiters judge the qualifications of job applicants.

To test the hypotheses in this paper, the authors use a policy capturing methodology that is widely used for analyses of human judgment and decision making. This design enables the authors to infer the way managers integrate different indices of selection in making decisions. During the period of time, the study was running, 95 participants with hiring experience in Chinese regions completed the task, and they were from several industries for the generalization of this study.

The results indicate that P-J fit, P-O fit and guanxi all have a unique impact on manager's hiring decisions in Chinese society, and P-J fit is weighted more heavily than P-O fit and guanxi.

This is the first study that integrates the Eastern and the Western perspectives, supplements the gap between selection and fit theory in the West and proves that P-J fit, P-O fit and Chinese guanxi all have individual influence on hiring decisions. “Guanxi” is the key factor that has great impact on the process of hiring decisions in Chinese societies compared with Western organizations.

The purpose of this paper is to design a low phase noise and high figure of merit, fully integrated, voltage‐controlled oscillator (VCO) which was fabricated in TSMC CMOS 0.18‐μm 1P6M process.

A differential PMOS cross‐coupled architecture VCO with the capacitive feedback technology was designed to increase the linearity of frequency tuning range and decrease the phase noise. Varactor determining the performance of tuning range is also a key component in the design of VCO. The authors adopt the accumulation‐mode MOS varactor. The output spectrum and the phase noise are measured by E5052A spectrum analyzer.

The VCO is successfully fabricated in TSMC RF CMOS 0.18um 1P6M process. The measured tuning range is from 10.875 GHz ∼ 11.1 GHz with control voltage from 0 to 1.5 V. The measured phase noise is as low as −120.42 dBc/Hz at 1 MHz offset and the high FOM is −189.5 dBc/Hz. The output spectrum is −10.51dBm with center oscillator frequency of 10.942 GHz. The core circuit without buffer consumes power of 15 mW from a 1.8 V supply voltage.

This paper shows a fully integrated CMOS LCVCO architecture using capacitive feedback technology with low phase noise and high figure of merit for OC‐192 SONET applications.

The purpose of this paper is to evaluate the effect of learning climate on customer-oriented behaviors by incorporating salespeople’s customer knowledge in the banks. It also explores the mediating role of customer knowledge between learning climate and customer-oriented behaviors.

A conceptual two-level model that links learning climate to customer-oriented behaviors was developed and tested using data collected from salespeople and customers in banks in Taiwan. Data from 444 customers involving 92 salespeople was collected via a questionnaire and analyzed using hierarchical linear modeling.

The results show that learning climate encourages salespeople to develop customer knowledge and customer-oriented behaviors, and that salespeople’s customer knowledge positively influences customer-oriented behaviors. This study also provides empirical support for the hypotheses that learning climate helps salespeople increase customer-oriented behaviors through improving salespeople’s customer knowledge.

The findings highlight the importance of enhancing learning climate and customer knowledge to enable banks to improve salespeople’s customer-oriented behaviors. This research also points to customer knowledge as mediating mechanisms that can explain the association between learning climate and customer-oriented behaviors in the sales context.

By integrating knowledge management with organizational learning research, this study evaluates the effect of learning climate on salespeople’s customer-oriented behaviors by incorporating their customer knowledge. In addition, the present study points to salespeople’s customer knowledge as one of several mediating mechanisms that explains the association between learning climate and customer-oriented behaviors.