Search results

It may be daunting for those who do not know or care for Shakespeare, but Othello is a compelling case study of destructive emotions in an organizational setting. Iago's chilling words from The Tragedy of Othello, The Moor of Venice are the title of this chapter, “I am not what I am”. Passed over for promotion, Iago wreaks havoc in the personal and professional life of the General who chose not to appoint him. We use this play as a case study of destructive emotions – specifically jealousy, anger, and shame – in an organizational hierarchy. The premise is that those who are passed over present a special managerial problem, one that we address at the end of the chapter after carefully looking at how revenge came to manifest from the emotions of the principal characters in the play. In addition, this chapter contributes to the growing literature on specific emotions as experienced in organizational life as well as advancing the links between management and the humanities by using one of Shakespeare's best-known tragedies as a case study.

The purpose of this paper is to analyze the mechanism and filter efficacy of accumulation generation operator (AGO)/inverse accumulation generation operator (IAGO) in the frequency domain.

The AGO/IAGO in time domain will be transferred to the frequency domain by the Fourier transform. Based on the consistency of the mathematical expressions of the AGO/IAGO in the gray system and the digital filter in digital signal processing, the equivalent filter model of the AGO/IAGO is established. The unique methods in digital signal processing systems “spectrum analysis” of AGO/IAGO are carried out in the frequency domain.

Through the theoretical study and practical example, benefit of spectrum analysis is explained, and the mechanism and filter efficacy of AGO/IAGO are quantitatively analyzed. The study indicated that the AGO is particularly suitable to act on the system's behavior time series in which the long period parts is the main factor. The acted sequence has good effect of noise immunity.

The AGO/IAGO has a wonderful effect on the processing of some statistical data, e.g. most of the statistical data related to economic growth, crop production, climate and atmospheric changes are mainly affected by long period factors (i.e. low-frequency data), and most of the disturbances are short-period factors (high-frequency data). After processing by the 1-AGO, its high frequency content is suppressed, and its low frequency content is amplified. In terms of information theory, this two-way effect improves the signal-to-noise ratio greatly and reduces the proportion of noise/interference in the new sequence. Based on 1-AGO acting, the information mining and extrapolation prediction will have a good effect.

The authors find that 1-AGO has a wonderful effect on the processing of data sequence. When the 1-AGO acts on a data sequence X, its low-pass filtering effect will benefit the information fluctuations removing and high-frequency noise/interference reduction, so the data shows a clear exponential change trends. However, it is not suitable for excessive use because its equivalent filter has poles at the non-periodic content. But, because of pol effect at zero frequency, the 1-AGO will greatly amplify the low-frequency information parts and suppress the high-frequency parts in the information at the same time.

Continuous training in professional teaching competencies has become one of the challenges of the school of the 21st century, especially if we refer to the mastery of digital competence. The aim of this study is to analyze the degree of digital competence of secondary school teachers of different areas of knowledge during the global pandemic of COVID-19 in the Galician autonomous community and obtain data that allow us to infer whether there is an association between individual variables such as age, gender, degree, work experience and their mastery of digital competence.

A cross-sectional descriptive quasi-experimental study is performed using an accidental sample of secondary school teachers from different fields of knowledge.

The different results obtained determine that the level of knowledge and use of digital media and tools is low. There are no significant differences depending on the variables analyzed, but it is necessary to establish specific continuing education plans for the improvement of digital competence in secondary school teachers to enable the effective use and management of information and communication technologies in future professionals.

Therefore, the findings of this study allow the development of educational interventions focused on increasing the digital competence of teachers, taking into account their individual characteristics.

This study aims to determine whether university students are successfully acquiring or improving skills related to teamwork through a variety of extra-curricular activities, such as sports, music, volunteering, international group work experiences and professional practices.

Descriptive statistics have been calculated to evaluate the normal behaviour of the variables. Accidental sampling was used via a cross-curricular study with a quasi-experimental design. An analysis of means was performed using the Student's t-test.

The results show the students to have highly developed interpersonal and communication skills, followed by adaptation and decision-making skills. The lowest scores corresponded to coordination and leadership skills. On the other hand, a significant correlation was established between extra-curricular activities and the acquisition of essential teamwork skills.

This study can confirm the important relation between extra-curricular activities and the acquisition of the teamwork skills that play a vital role in the overall development of our students, as well as for their integration into the job market, with particular emphasis on the acquisition of leadership skills which students are most lacking in, but which have shown to improve with any of the proposed activities. The promotion of the extra-curricular activities by the University, could greatly support our students' soft skills acquisition and complement their education.

Given that the use of Google Trends data is helpful to improve forecasting performance, this study aims to investigate whether the precision of forecast combination can benefit from the use of Google Trends Web search index along with the encompassing set.

Grey prediction models generate single-model forecasts, while Google Trends index serves as an explanatory variable for multivariate models. Then, three combination sets, including sets of univariate models (CUGM), all constituents (CAGM) and constituents that survive the forecast encompassing tests (CSET), are generated. Finally, commonly used combination methods combine the individual forecasts for each combination set.

The tourism volumes of four frequently searched-for cities in Taiwan are used to evaluate the accuracy of three combination sets. The encompassing tests show that multivariate grey models play a role to be reckoned with in forecast combinations. Furthermore, the empirical results indicate the usefulness of Google Trends index and encompassing tests for linear combination methods because linear combination methods coupled with CSET outperformed that coupled with CAGM and CUGM.

With Google Trends Web search index, the tourism sector may benefit from the use of linear combinations of constituents that survive encompassing tests to formulate business strategies for tourist destinations. A good forecasting practice by estimating ex ante forecasts post-COVID-19 can be further provided by scenario forecasting.

To improve the accuracy of combination forecasting, this research verifies the correlation between Google Trends index and combined forecasts in tourism along with encompassing tests.

過去的研究顯示 Google 搜尋趨勢資料有助於改善旅遊需求預測的準確度，本研究就此進一步探討 Google 搜尋趨勢網頁搜尋指標與涵蓋性檢定的使用對於組合預測準確度所造成的影響。

本研究以 Google 搜尋趨勢指標做為多變量灰色預測模式的解釋變數，並以單變量與多變量灰色模式產生各別預測值。在分別產生由所有的單變量模式 (CUGM)所有的模式 (CAGM), 以及經過涵蓋性檢定所留存下來之模式 (CSET) 所組成之集合後，就各別的組合集以常用的組合方法產生預測值。

以台灣的四個熱搜旅遊城市的旅遊人數進行三個組合集的預測準確度分析。涵蓋性檢定顯示多變量灰色模式在組合預測中扮演重要的角色，而結果亦呈現線性組合方法在 CSET優於在 CUGM 與 CAGM 的準確度，突顯搜尋趨勢指標與涵蓋性檢定對於線性組合方法的有用性。

藉由 Google 搜尋趨勢網頁搜尋指標與涵蓋性檢定，旅遊部門應可透過線性組合方法的預測規劃旅遊目的地的經營策略。新冠疫情下於各季的事前預測亦可結合情境預測具體呈現。

為提升組合預測在旅遊需求的預測準確度，本研究結合涵蓋性檢定以分析 Google 搜尋趨勢指標與組合預測準確度之間的關聯性。

旅遊需求,涵蓋性檢定,Google 搜尋趨勢,灰色預測,組合預測

研究型论文

Dado que el uso de los datos de Google Trends es útil para mejorar la precisión de las predicciones, este estudio examina si el uso del índice de búsqueda web de Google Trends combinado con la agregación de relevancia puede mejorar la precisión del predictor.

El modelo predictivo gris genera predicciones bajo un único modelo, mientras que el modelomultivariado utiliza el indicador Google Trends como variable explicativa. Se generaron tresensamblajes generales, incluido el Modelo armónico único (CUGM), los ensamblajes de todos loscomponentes (CAGM) y la prueba de presencia de componentes con predicción (CSET). Laspredicciones individuales encada grupo luego se combinan utilizando métodos de correlación deuso común.

Utilizando el número de turistas en las cuatro ciudades más investigadas de Taiwán, los tresgrupos combinados se clasificaron según su precisión. Las pruebas incluidas muestran que losmodelos multivariados en escala de grises son importantes para la predicción. Además, losresultados de las pruebas muestran que el índice de Google Trends y las pruebas que incluyenmétodos de suma lineal son útiles porque los métodos combinados con CSET funcionan majorque los métodos combinados con CSET. CAGM y VCUG.

La industria de viajes puede usar el índice de búsqueda web de Google Trends para desarrollarestrategias comerciales para atracciones basadas en un conjunto lineal de componentes.

Con el objetivo de mejorar la precisión de los pronósticos agregados, este estudio investiga larelación entre el índice de tendencias de Google y las expectativas generales de viaje junto con laevidencia global.

Demanda de viajes, Experiencia global, Tendencias de Google, Predicción gris

Trabajo de investigación

The purpose of this paper is to provide a brief history and perspective on the influence of McGregor's Theory Y on research about and practice of management and leadership. Although best known for Theory X and Y, Douglas McGregor involved himself in more than theory. He was for a time President of Antioch College, a builder of a strong academic program of management and organizational behavior in the Sloan School of Management at Massachusetts Institute of Technology , and a pioneer in the origination of organization development.

The legacy of Theory Y can be characterized as a set of beliefs that are antecedents to participative and involvement modes of management, that these modes are associated with success backed by considerable evidence, yet are not practiced by the majority of people in management and leadership positions. Moreover, the track record of management and leadership in the USA is not exactly exemplary, for example, at least half of those in management fail, and more than 70 percent of employees surveyed in a variety of contexts state that the main cause of stress for them at work is their boss.

With respect to Theory Y, McGregor's intent was to show the relativity of authority, and that participative management was consistent with Y, but these assumptions did not necessarily lead to a specific style of management.

This apparent paradox of evidence that involvement forms of management pays off yet few practice participation begs the question of why. Among many reasons the following stand out: executives do not trust the evidence at least not for their levels (It is okay for first line supervisors), and besides participative forms of management require a particular set of skills and competence time (too much one suspects), and patience. Many executives do not have the requisite skills nor do they have the patience to take the time that such forms of management and leadership require.

Robust political economy (RPE) is a research program that combines insights from Austrian economics and public choice to evaluate the performance of institutions in cases of limited knowledge and limited altruism, or “worst-case scenarios.” Many critics of RPE argue that it is too narrowly focused on the bad motivations and inadequacies of social actors while smuggling in classical liberal normative commitments as part of a purported solution to these problems. This chapter takes a different tack by highlighting the ways that RPE as currently understood may not be robust against particularly bad conduct. It suggests that depending on the parameters of what constitutes a worst-case scenario, classical liberal institutions, especially a minimal state, may turn out to be less robust than some conservative or social democratic alternatives.

The justification of punishment is an age-old debate which continues unresolved. In late twentieth century several attempts were made to reconcile the two opposing justifications: retributivism and consequentialism. But these attempts focused narrowly on merely one manifestation of punishment, i.e.: criminal punishment carried out by the state. To the extent that these mixed justifications are successful, they relate to only one (undoubtedly important) manifestation of punishment. But clearly punishment can occur in many different institutional contexts, and the institutions in each context vary dramatically in complexity and relevance. I recommend analyzing punishment in its manifold manifestations.

LET'S VARY LITERACEE with a little bibliographic burglaree. If you suddenly feel like humming The Pirates of Penzance or recollecting Gilbert and Sullivan, you are closely attuned to the bibliographic thoughts in my mind. Literary allusions are the rich overtones that make reading and writing a grand collaboration and a happy pursuit. An author may conscientiously write to convey ideas, but if a cut above the average, he always strives as did H. L. Mencken to express his ideas ‘in suave and ingratiating terms, and to discharge them with a flourish, and maybe with a phrase of pretty song’. His creative efforts will be mostly wasted, however, if his readers lack the requisite literary background and sophistication that would enable them to join in his game and share his earnest effusions. Literacy is never enough; a young child can read and understand the six one‐syllable words ‘who steals my purse, steals trash’, but that same child can grow to be a mighty old man without ever fully comprehending the sentence unless he reads Othello and studies Iago's presumptuous remarks on ‘Good name in man and woman’.

Prediction problems raised in uncertain environments require different solution approaches such as grey prediction models, which consider uncertainty in information and also enable the use of small data sets. The purpose of this paper is to investigate the comparative performances of grey prediction models (GM) and Markov chain integrated grey models in a demand prediction problem.

The modeling process of grey models is initially described, and then an integrated model called the Grey-Markov model is presented for the convenience of applications. The analyses are conducted on a monthly demand prediction problem to demonstrate the modeling accuracies of the GM (1,1), GM (2,1), GM (1,1)-Markov, and GM (2,1)-Markov models.

Numerical results reveal that the Grey-Markov model based on GM (2,1) achieves better prediction performance than the other models.

It is thought that the methodology and the findings of the study will be a significant reference for both academics and executives who struggle with similar demand prediction problems in their fields of interest.

The novelty of this study comes from the fact that the GM (2,1)-Markov model has been first used for demand prediction. Furthermore, the GM (2,1)-Markov model represents a relatively new approach, and this is the second paper that addresses the GM (2,1)-Markov model in any area.