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Exponential growth in online video content makes viewing choice and video promotion increasingly challenging. While explicit recommendation systems have value, they inherently distract the user from normal behaviour and are open to numerous biases. To enhance user interest evaluation accuracy, the purpose of this paper is to comprehensively examine the relationship between implicit feedback and online video content, and reviews gender differentials in the interest indicated by a comprehensive set of viewer responses.

This paper includes 200 useable observations based on an experiment of user interaction with the Youku platform (one of the largest video-hosting websites in China). Logistic regression was employed for its simple interpretation to test the proposed hypotheses.

The findings demonstrate gender differentials in cursor movement behaviour, explainable via well-studied splits in personality, biological factors, primitive behaviour and emotion management. This work offers a solution to the sparsity of work on implicit feedback, contributing to the literature that combines explicit and implicit feedback.

This study offers a launch point for further work on human–computer interaction, and highlights the importance of looking beyond individual metrics to embrace wider human traits in video site design and implementation.

This paper links implicit feedback to online video content for the first time, and demonstrates its value as an interest capturing tool. By reviewing gender differentials in the interest indicated by a comprehensive set of viewer responses, this paper indicates how user characteristics remain critical. Consequently, this work signposts highly fruitful directions for both practitioners and researchers.

An important issue in the management of a web‐based user community, where users are registered to a web portal, is to identify patterns of users' interest. In this context, the users' feedback plays a major role. The purpose of this paper is to define a novel framework analysis for managing the feedback given by registered visitors of a web site.

The paper presents a new technique to integrate the feedback explicitly given by users into already existing user profiles. The authors introduce the novel concepts of scope, filtering, and relevance profiles for managing users' feedback. The new concept of Relevance Profile (RP) is defined.

Using the framework, the authors were able to discover patterns of usage of registered users of a web site.

The practical applicability of the approach is validated by a use case study showing how the framework can be used with a real web site. The authors used Gugubarra as a reference system, a prototype for creating and managing web user profiles, developed by the DBIS group at the Goethe‐University of Frankfurt.

A new way to integrate the user feedback into interest profiles and a novel framework to analyze and discover patterns of interests are presented. The paper is an extended version (more than 50 per cent novel material) of a previous paper presented at the iiWAS2010 conference.

The purpose of this paper is to improve users’ search results relevancy by manipulating their explicit feedback.

CoRRe – an explicit feedback model integrating three popular feedback, namely, Comment-Rating-Referral is proposed in this study. The model is further enhanced using case-based reasoning in retrieving the top-5 results. A search engine prototype was developed using Text REtrieval Conference as the document collection, and results were evaluated at three levels (i.e. top-5, 10 and 15). A user evaluation involving 28 students was administered, focussing on 20 queries.

Both Mean Average Precision and Normalized Discounted Cumulative Gain results indicate CoRRe to have the highest retrieval precisions at all the three levels compared to the other feedback models. Furthermore, independent t-tests showed the precision differences to be significant. Rating was found to be the most popular technique among the participants, producing the best precision compared to referral and comments.

The findings suggest that search retrieval relevance can be significantly improved when users’ explicit feedback are integrated, therefore web-based systems should find ways to manipulate users’ feedback to provide better recommendations or search results to the users.

The study is novel in the sense that users’ comment, rating and referral were taken into consideration to improve their overall search experience.

In the article ‘Approaches to Social Skills Training’ we outlined three levels at which social skills training can be conducted in terms of learning by Thinking, Doing and Feeling. We pointed out that often trainers concentrate on learning in one or two of these areas rather than mixing the approaches and integrating them in one learning process. We summarised some of the ingredients and consequences of these three approaches and presented a broad spectrum of training styles from which the social skills trainer can select. Finally we pointed out the need to distinguish between training and therapy at one end of the spectrum, and suggested how the trainer could establish a clear boundary. In this article we examine feedback in detail as one aspect of social skills training and we explore the range of choices the trainer has in this area.

Implicit motives are capacities to experience specific types of incentives as rewarding and specific types of disincentives as aversive (Atkinson, 1957; Schultheiss, 2008). Because implicit motives determine which stimuli are affectively “hot”, they also orient the person's behavior toward those stimuli, energize behavior aimed at attaining (or avoiding) them, and select stimuli that predict their proximity and behaviors that are instrumental for attaining (or avoiding) them (McClelland, 1987).

This study aims to examine the influence of task interdependence on team members’ Moqi in virtual teams in China. The authors also aim to identify virtual collaboration as a mediator and distributive justice climate as a moderator in this relationship.

The data were collected from a sample of 87 virtual teams (including 349 individuals) from various Chinese companies through a three-wave survey. Hierarchical regression analysis, path analysis, bootstrapping method and multiple validity tests were used to examine the research model.

In virtual teams in China, task interdependence has a significantly positive influence on team members’ Moqi; Virtual collaboration mediates the relationship between task interdependence and team members’ Moqi; The distributive justice climate positively moderates the relationship between task interdependence and virtual collaboration, as well as the indirect effect of virtual collaboration on the relationship between task interdependence and team members’ Moqi.

In virtual teams, leaders can facilitate team members’ Moqi by designing highly interdependent tasks, encouraging team members to engage in virtual collaboration and cultivating a climate of high attention distributive justice.

This is one of the first studies to pay to the Moqi among team members rather than supervisor-subordinate relationships and further examine how team members’ Moqi is predicted by task interdependence via the mediation of virtual collaboration with the distributive justice climate playing a moderating role.

A recommendation algorithm is typically applied to speculate on users’ preferences based on their behavioral characteristics. The purpose of this paper is to provide a systematic review of recommendation systems by collecting related journal articles from the last five years (i.e. from 2014 to 2018). This paper aims to study the correlations between recommendation technologies and e-learning systems.

The paper reviews the relevant articles using five assessment aspects. A coding scheme was put forward that includes the following: the metrics for the e-learning system, the evaluation metrics for the recommendation algorithms, the recommendation filtering technology, the phases of the recommendation process and the learning outcomes of the system.

The research indicates that most e-learning systems will adopt the adaptive mechanism as a primary metric, and accuracy is a vital evaluation indicator for recommendation algorithms. In existing e-learning recommender systems, the most common recommendation filtering technology is hybrid filtering. The information collection phase is an important process recognized by most studies. Finally, the learning outcomes of the recommender system can be achieved through two key indicators: affections and correlations.

The recommendation technology works effectively in closing the gap between the information producer and the information consumer. This technology could help learners find the information they are interested in as well as send them a valuable message. The opportunities and challenges of the current study are discussed; the results of this study could provide a guideline for future research.

Noting that accurate information needs overlay the entire intelligence cycle from planning to reporting, the authors propose a new concept to fine‐tune the process of electronically gathering information. Since most tools are nondiscriminatory in information gathering, a new concept is needed to assist managers at all levels of the organization. The proposed multi‐class interest profile (M‐CLIP) provides the capability of expanding the representation of interests to reflect the assorted areas that make up a manager’s information needs. The M‐CLIP covers project, organizational, and industry class interest areas. Each area is customizable to make the search pertinent to the user while considering the need for both recall and precision. Supporting features, such as profile expansion, and fine‐tuning are also considered.

Web personalization has been studied in different streams of research such as Marketing, Human Computer Interaction and Computer Science. However, an information systems perspective of web personalization research is very scarcely visible in this body of knowledge. This research review seeks to address two important questions: how has web personalization evolved as an integrative discipline? How has web personalization been treated in IS literature and where should researchers focus next?

The paper intently follows an information systems perspective in its thematic classification of web personalization research which is consistent with the early conceptualization of information systems by logically mapping IS categories into web personalization research streams. Articles from 100+ journals were analyzed and important concepts related to web personalization were classified from an information systems perspective.

Surrounding the theme of web personalization two parallel streams of research evolved. First stream consisted of internet business models, computer science algorithms and web mining. Second stream focussed on human computer Interaction studies, user modelling and targeted marketing. Future information systems researchers in web personalization must focus on four important areas of social media, web development methodologies, emerging Internet accessing gadgets and domains other than e‐Commerce.

Web personalization has been studied previously in separate research streams. But no integrated view from different research streams exists. Although research interest in web mining has been growing as evidenced by growing number of publications an information systems perspective of web personalization research is very scarcely visible in the body of knowledge. The authors intently follow an information systems perspective in their thematic classification of web personalization research which is consistent with the early conceptualization of information systems by logically mapping IS categories into web personalization research streams. This thematic segregation of different research streams into IS framework makes our study distinct from other early reviews. They also identify four important areas where future IS researchers should focus on.

Delivering negative feedback to employees is highly problematic for managers. Negative feedback is important in generating improvements in employee performance, but likely to generate adverse employee reactions. However, if managers do not address poor performance, good performers may become demoralized or exit the organization. The purpose of this paper is to investigate how managers communicate negative feedback and the factors that drive their choice of tactic.

The authors use interview data from practicing line managers with experience in delivering negative feedback to learn whether their tactic choices are consistent with Implicit (“best practice”) or Contingency (“best fit”) theory.

The authors identify five negative feedback tactics: evidence, emotive and communication tactics are foundation tactics while evidence + communication and evidence + emotive tactics are bundles of the foundation tactics. Managers apply a “best fit” approach from a set of “best practice” negative feedback options. The choice of negative feedback tactic is driven by the manager’s assessment of the “best fit” with the employee’s personality.

Most of the managers believed that their negative feedback tactic had been effective. Future researchers should investigate which negative feedback tactics employees regard as most effective.

A best fit approach to the delivery of negative feedback requires organizations to give managers discretion in the delivery of negative feedback. Managers may mis-assess fit which can undermine the effectiveness of the appraisal process.

The authors focus on how negative feedback is communicated by managers. Existing research focusses on reactions to negative feedback without taking into account how it is delivered.