Review Product Distribution Through Different Channels

This study aims to investigate the literature in product distribution and channel competition. In this study, past work related to the product distribution through different channels is extensively reviewed. Based on the channel differentiation, channel competition is also reviewed. Finally, the study proposes that the future research may focus on helping the producer make a decision whether to sell the product through its own direct channel (online) through a physical channel or both based on the difference between these two channels.

Collaborative Filtering Technical Comparison in Implicit Data

Recommendation systems have become a necessity due to the mass of information accumulated for each site. For this purpose, there are several methods including collaborative filtering and content-based filtering. For each approach there is a vast list of procedural choices. The work studies the different methods and algorithms in the field of collaborative filtering recommendation. The objective of the work is to implement these algorithms in order to compare the different performances of each one; the tests were carried out in two datasets, book crossing and Movieslens. The use of a data set benchmark is crucial for the proper evaluation of collaborative filtering algorithms in order to draw a conclusion on the performance of the algorithms.

Advancing Knowledge About Sexual Harassment Is a Critical Aspect of Organizational Development for All Employees

Sexual harassment remains prevalent in the workplace as well as a significant financial burden for organizations. Given the cost associated with sexual harassment, reputational damage, and internal disturbances, sexual harassment is a mounting apprehension because senior managers are responsible for bestowing an organizational culture that is intolerant to such engrossed behavior. It is imperative to explore how workplace stories and office talk are influenced by organizational culture and how organizational culture affects the workers' perceptions of Me Too and consequences of sexual harassment. Understanding the phenomenon of sexual harassment at work is vital to shaping organizational culture, behavior, and policies aimed at reducing sexual misconduct at work. Existing research indicates that 81% of women and 43% of men have experienced an aspect of sexual harassment. This study uses a content analysis exploration of the literature to better understand the impact and solutions to sexual harassment in the workplace.

Knowledge Diversity and Employee Outcomes

This research investigates the impact of perceived knowledge diversity (PKD) on job outcomes such as job satisfaction (JS) and job performance (JP). Next, this study also explores the moderating role of workforce diversity management (WFDM) on the relationship between PKD and employee job outcomes (EJO). SPSS and AMOS-SEM are used to analyze the data. The findings show that PKD is positively related to JS and negatively associated with JP. WFDM moderates the relationship between PKD and EJO (JS and JP). There are many studies related to diversity and its management, but most of the studies are from an American perspective that deals with diversity related to age, gender, race, ethnicity, etc. (surface-level diversity). The study in diversity in knowledge, value, personality, belief, etc. (deep-level diversity) is scanned in the literature. This study aims to address this void, particularly investigating knowledge diversity in research by establishing how WFDM moderates the relationship between PKD and employee outcomes.

Indoor Air Quality Monitoring Systems

Research activity in the field of monitoring indoor quality systems has increased dramatically in recent years. Monitoring closed areas can reduce health-related risks due to poor or contaminated air quality. In the current COVID pandemic, the population has observed that improving ventilation in the closed area can significantly reduce infection risk. However, the significance of air quality statistics makes highly accurate real-time monitoring systems vital. In this paper, several researchers' protocols and the methodologies for monitoring a good high indoor air quality system are presented. The majority of the reviewed works are aimed to reduce air pollution levels of the atmosphere. The vast majority of the identified works utilized IoT and WSN technology to fix the partial access to sensed data, high cost, and non-scalability of conventional air monitoring systems. Furthermore, ad-hoc approaches are predominantly used to help society change its attitude and impose corrective actions to improve air quality. This paper presents a short but comprehensive review of several researchers works with different approaches to ecological trend analysis capabilities, drawing on existing literature works. Overall, the findings highlight the need for developing systematic protocols for these systems and establishing smart air quality monitoring systems capable of measuring pollutant concentrations in the air.

Reducing Replica Update Cost in Replicated DRTDBS

Replication techniques have drawn a great deal of appeal in the real-time database system (RTDBS). In this technique, the replication protocol (RPCL) has been studied as one of the primary technologies. Existing RPCL causes improper resource utilization and suffers from large communication costs. Hence, the objective is to propose a solution that effectively utilizes the system resource and decreases the large communication cost. The proposed data mining algorithm identifies the frequently accessed data items, their related data access sequence, and replicate those data items on the demanded replica sites. Additionally, mutual consistency among newly created data replica and existing data replica gets satisfied with the proposed RPCL. The experimental result shows that the proposed solutions improve the performance of the system by reducing the issue of unnecessary replica updation, unnecessary storage utilization, and unnecessary bandwidth utilization.

Using Latent Fine-Grained Sentiment for Cross-Domain Sentiment Analysis

The authors investigate the problem task of multi-source cross-domain sentiment classification under the constraint of little labeled data. The authors propose a novel model which is capable of capturing both sentiment terms with strong or weak polarity from various source domains which are useful for knowledge transfer to unlabeled target domain. The authors propose a two-step training strategy with different granularities helping the model to identify sentiment terms with different degrees of sentiment polarity. Specifically, the coarse-grained training step captures the strong sentiment terms from the whole review while the fine-grained training step focuses on the latent fine-grained sentence sentiment which are helpful under the constraint of little labeled data. Experiments on a real-world product review dataset show that the proposed model has a good performance even under the little labeled data constraint.

Knowledge Integration, Green Innovation, and Financial Performance

Developing green products and improving green production processes are critical in enhancing a company's environmental performance and sustainability. The sophisticated processes involved in green innovation have a steep learning curve, but its navigation has become essential for companies engaged in green operations. Drawing on the theories of organizational learning and dynamic capabilities, this paper presents our empirical analysis of 231 Chinese firms and suggests that the impact a firm's knowledge process integration has on environmental performance is mediated by its green product and process innovations capability. This study should help managers improve green innovation and financial performance.

Measuring Relative Effectiveness of Service Quality Factors Affecting Quality of Higher Education in India

The existing competitive environment in the education sector in India has forced higher education institutes to adopt ‘students as a customer' approach in educational delivery. The extant literature supports seven dimensions of service quality namely input quality, curriculum, academic facilities, industry interaction, interaction quality, support facilities, and nonacademic processes. The objective of the study was to measure the relative effectiveness of service quality factors as perceived by the Indian students. Focus group interviews were conducted with a group of technical and management students. Stratified judgmental sampling was used for the data collection. In the current study, it was observed that the factor of program quality plays a more dominant role than the factor of quality of life in determining the service quality in institutes of higher education. It is further observed that in the sub-dimension of program quality, the factor of curriculum and academic facilities are relatively more dominant than the factor of input quality.

Successful Use of Knowledge-Based Systems for Collaboration in Higher Education

Knowledge-based systems are forms of computerized tools designed specially to help in many aspects specifically in collaboration activities. Despite the importance of these tools in generating, exchanging, and sharing the information and knowledge, many institutions, especially educational ones, are not interested in them, because of insufficient knowledge of the role of knowledge-based systems and their effectiveness in the academic events. The basic purposes of this paper are to find the role of knowledge-based systems collaboration tools (KBSCT) in higher educational institutions and to recognize in what way institution and student performance are affected by implementing KBSCT. The research questionnaires distributed to 100 participants, but only 67 were used and included in the frequency analysis. The results added a contribution in terms of identifying how the KBSCT effect on the higher educational institution and student's decisions.