Search results

The purpose of this study is to examine student sentiments regarding high-quality vs low-quality teaching.

This study uses a text mining technique to identify the positive and negative patterns of student sentiments from student evaluations of teaching (SET) provided on Ratemyprofessors.com. After identifying the key positive and negative sentiments, this study performs generalized linear regressions and calculates cumulative logits to analyze the impact of key sentiments on high- and low-quality teaching.

Results from 6,705 SET provided on Ratemyprofessors.com indicated that students express different sets of sentiments regarding high- vs low-quality teaching. In particular, the authors found positive sentiments such as passionate, straightforward, accessible, hilarious, sweet, inspiring and clear to be predictive of high-quality teaching. Additionally, negative sentiments such as disorganized, rude, difficult, confusing and boring were significantly related to low-quality teaching.

This study is one of the first few studies confirming that high- and low-quality teaching are not completely opposite to each other from the student’s perspective. That is, the presence of high-quality teaching does not necessarily mean the absence of low-quality teaching. As such, this study provides an important theoretical base for future researchers who wish to explore approaches for improving faculty teaching in the higher education setting. Additionally, this study offers educators some recommendations that may help students experience positive sentiments while minimizing negative sentiments.

Although prior research has documented substantive knowledge of the benefits of high-performance work systems (HPWS), results regarding both sides of HPWS are inconsistent. To reconcile these inconsistencies, the authors identified two specific HR attributions—employee well-being HR attribution and performance HR attribution, and examined their roles in the relationship between team-level HPWS and employees' thriving at work and emotional exhaustion.

The authors collected multi-source data from 36 team leaders and 181 individuals. Given the nested nature of the data, the authors used Mplus 7.4 to conduct multilevel structural equation modeling for hypothesis testing.

The results showed that team-level HPWS and employee well-being HR attribution interact to affect psychological availability, which subsequently promotes thriving at work. However, team-level HPWS and employee performance HR attribution do not interact to influence role overload/psychological availability; team-level HPWS and employee well-being HR attribution do not interact to affect role overload.

Current literature has overlooked identifying key contingencies for both sides of HPWS effects on employee outcomes. Therefore, this study developed a mediated moderation model and incorporated HR attributions to explore two distinct pathways by which HPWS affects employees' thriving at work and emotional exhaustion. The present study helps to reconcile the inconsistent findings regarding the HPWS double-edged sword nature. In addition, the authors focused on HPWS at the team level, which is also underexplored in the existing HPWS research.

This study aims to understand the epistemic foundation of the classification applied in the first Chinese library catalogue, the Seven Epitomes (Qilue).

Originating from a theoretical stance that situates knowledge organization in its social context, the study applies a multifaceted framework pertaining to five categories of textual data: the Seven Epitomes; biographical information about the classificationist Liu Xin; and the relevant intellectual, political, and technological history.

The study discovers seven principles contributing to the epistemic foundation of the catalogue's classification: the Han imperial library collection imposed as the literary warrant; government functions considered for structuring texts; classicist morality determining the main classificatory structure; knowledge perceived and organized as a unity; objects, rather than subjects, of concern affecting categories at the main class level; correlative thinking connecting all text categories to a supreme knowledge embodied by the Six Classics; and classicist moral values resulting in both vertical and horizontal hierarchies among categories as well as texts.

A major limitation of the study is its focus on the main classes, with limited attention to subclasses. Future research can extend the analysis to examine subclasses of the same scheme. Findings from these studies may lead to a comparison between the epistemic approach in the target classification and the analytic one common in today's bibliographic classification.

The study is the first to examine in depth the epistemic foundation of traditional Chinese bibliographic classification, anchoring the classification in its appropriate social and historical context.

The aim of this paper is to study the dynamic vibrations of embedded double‐walled carbon nanotubes (DWCNTs) subjected to a moving harmonic load with simply supported boundary conditions.

The model of DWCNTs is considered as an Euler‐Bernoulli beam with waviness along the length, which is more accurate than the straight beam in previous works. Based on the nonlocal beam theory, the governing equations of motion are derived by using the Hamilton's principle, and then the separation of variables is carried out by the Galerkin approach, leading to two second‐order ordinary differential equations (ODEs).

The influences of the nonlocal parameter, the amplitude of the waviness, the surrounding elastic medium, the material length scale, load velocity and van der Waals force on the nonlinear vibration of DWCNTs are important.

The dynamic responses of DWCNTs are obtained by using the precise integrator method to ordinary differential equations.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

The purpose of this paper is to demonstrate the main advantages of integrating bottleneck theory, action learning and transformation capabilities to phenomenon and process analysis systems.

This paper selects three typical cases, using grounded theory standardized coding procedures, and selects exploratory case study approach.

Inward small and medium manufacturing enterprises use the bottleneck breakthrough program and provide a correct knowledge input for action learning. Action learning provides a strong guarantee that for the implementation of bottleneck breakthrough program, programming and action learning are required to continually solve problems and achieve goals in the process.

The authors used inward manufacturing small- and medium-sized enterprises as research subjects The authors did not analysis the role of knowledge services; the future studies could explore how to improve the performance through the transformation value co-creation.

Because of the lack of resources and capacity, small- and medium-sized enterprise adopt appropriate micro-innovation and continuous micro-transformation to break the bottleneck stage and achieve small victories.

Learning and development enterprises are not only through multinational clients which restructuring enhance the learning capacity of the international M & A path. It does not conduct thorough and comprehensive change, and also not related to the structural of readjustment organization. In fact, the radical change and transformation strategy is different than other strategies.