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Intelligence level of a crowd network is defined as the expected reward of the network when completing the latest tasks (e.g. last N tasks). The purpose of this paper is to improve the intelligence level of a crowd network by optimizing the profession distribution of the crowd network.

Based on the concept of information entropy, this paper introduces the concept of business entropy and puts forward several factors affecting business entropy to analyze the relationship between the intelligence level and the profession distribution of the crowd network. This paper introduced Profession Distribution Deviation and Subject Interaction Pattern as the two factors which affect business entropy. By quantifying and combining the two factors, a Multi-Factor Business Entropy Quantitative (MFBEQ) model is proposed to calculate the business entropy of a crowd network. Finally, the differential evolution model and k-means clustering are applied to crowd intelligence network, and the species distribution of intelligent subjects is found, so as to achieve quantitative analysis of business entropy.

By establishing the MFBEQ model, this paper found that when the profession distribution of a crowd network is deviate less to the expected distribution, the intelligence level of a crowd network will be higher. Moreover, when subjects within the crowd network interact with each other more actively, the intelligence level of a crowd network becomes higher.

This paper aims to build the MFBEQ model according to factors that are related to business entropy and then uses the model to evaluate the intelligence level of a number of crowd networks.

Detrimental effects of diversity in workgroups has frequently been observed but research identifying the factors that lead to negative or positive effects in heterogeneous groups is lacking. The Perceived Dissimilarity Openness Moderator Model provides one explanation of the process by which diversity influences group affective, behavioral, and cognitive outcomes. Specifically the model identifies individual, group, and organizational openness as moderating the effects of diversity in workgroups. In this paper evidence is provided from a field study that increased openness to perceived dissimilarity leads to better outcomes in newly formed groups. This study also constitutes a significant building block toward the development of theory concerning the moderating variables of the relationship between diversity and group processes, and outcomes of organizations.

This paper aims to optimize and evaluating the performance of the crowd networks through analyzing their information sharing patterns. That is, in a crowd network, the qualities of accomplishing tasks are highly dependent on the effective information sharing among intelligent subjects within the networks. Hence, proposing an adaptive information-sharing approach can help improve the performance of crowd networks on accomplishing tasks that are assigned to them.

This paper first introduces the factors that affect effectiveness of information-sharing pattern: the network topology, the resources owned by intelligent subjects and the degree of information demand. By analyzing the correlation between these factors and the performance of crowd networks, an Adaptive Information Sharing Approach for Crowd Networks (AISCN approach) is proposed. By referring to information needed for accomplishing the historical tasks that are assigned to a crowd network, the AISCN approach can explore the optimized information-sharing pattern based on the predefined composite objective function. The authors implement their approach on two crowd networks including bee colony and supply chain, to prove the effectiveness of the approach.

The shared information among intelligent subjects affects the efficiency of task completion in the crowd network. The factors that can be used to describe the effectiveness of information-sharing patterns include the network topology, the resources owned by intelligent subjects and the degree of information demand. The AISCN approach used heuristic algorithm to solve a composite objective function which takes all these factors into consideration, so that the optimized information-sharing pattern can be obtained.

This paper introduces a set of factors that can be used to describe the correlation between information-sharing pattern and performance of crowd network. By quantifying such correlation based on these factors, this paper proposes an adaptive information-sharing approach which can explore the optimized information-sharing pattern for a variety of crowd networks. As the approach is a data-driven approach that explores the information-sharing pattern based on the network’s performance on historical tasks and network’s characteristics, it is adaptive to the dynamic change (change of incoming tasks, change of network characteristics) of the target crowd network. To ensure the commonality of the information-sharing approach, the proposed approach is not designed for a specific optimization algorithm. In this way, during the implementation of the proposed approach, heuristic algorithms can be chosen according to the complexity of the target crowd network.

The purpose of this paper is to investigate the role of product and customer dimensions in the contribution of brand experience to the formation of true brand loyalty. The dimensions included are brand credibility, affective commitment and involvement. Synthesising past studies, the researcher proposes brand credibility and affective commitment to mediate the relationship between brand experience and true brand loyalty. Furthermore, the researcher investigates the variation in hierarchical pattern, i.e. brand experience-brand credibility affective commitment-true brand loyalty, under different levels of involvement.

The variations in hierarchy were compared by design. The authors investigated the variations in hierarchy on the basis of products which belong to different level of involvement, on the basis of individual differences in involvement, and on the basis of the interaction of product involvement and subject involvement. Multi-group invariance tests in SEM were used to explore model variations.

The hierarchy-of-effect model was found to vary based on the level of product involvement, subject involvement and interaction involvement. Three patterns of hierarchy have been observed: the first pattern was observed in high-high groups (both product involvement and subject involvement were high), the second pattern was observed in low-low groups (both product and subject involvements were low) and the third pattern among high-low or low-high groups.

The variation observed highlights the need to segment the market by interaction involvement. This would be useful for managers engaged in building sustainable consumer-brand relationships.

This study considered the interaction of product approach and subject approach in defining involvement which is rarely attempted in research. The study also integrates the variations in the role of customer dimensions, namely involvement, brand credibility and affective commitment with the relationship between the central constructs brand experience and true brand loyalty. The variations observed are among a socio-economically homogeneous sample of respondents.

The purpose of this paper is to propose a seamless active interaction control method integrating electromyography (EMG)-triggered assistance and the adaptive impedance control scheme for parallel robot-assisted lower limb rehabilitation and training.

An active interaction control strategy based on EMG motion recognition and adaptive impedance model is implemented on a six-degrees of freedom parallel robot for lower limb rehabilitation. The autoregressive coefficients of EMG signals integrating with a support vector machine classifier are utilized to predict the movement intention and trigger the robot assistance. An adaptive impedance controller is adopted to influence the robot velocity during the exercise, and in the meantime, the user’s muscle activity level is evaluated online and the robot impedance is adapted in accordance with the recovery conditions.

Experiments on healthy subjects demonstrated that the proposed method was able to drive the robot according to the user’s intention, and the robot impedance can be updated with the muscle conditions. Within the movement sessions, there was a distinct increase in the muscle activity levels for all subjects with the active mode in comparison to the EMG-triggered mode.

Both users’ movement intention and voluntary participation are considered, not only triggering the robot when people attempt to move but also changing the robot movement in accordance with user’s efforts. The impedance model here responds directly to velocity changes, and thus allows the exercise along a physiological trajectory. Moreover, the muscle activity level depends on both the normalized EMG signals and the weight coefficients of involved muscles.

In order to test the impact of system and domain knowledge on search behaviour in an online catalogue, an experiment was set up in a university library where students from three specialisation areas performed a number of search tasks in the online catalogue. The subjects differed in the amount of domain and system knowledge. In two sessions the subjects performed searches inside and outside their ‘own’ domain. During the first session all subjects had little system knowledge. After the first session, half of the group received instruction in catalogue use and the other half did not. To observe whether the induced differences in system knowledge had effects on the search performance, a second session was carried out. Subjects' search behaviour was videotaped and their comments recorded (they were encouraged to think aloud). Results show the the amount of system knowledge had a significant effect on search time as well as on the number of search patterns observed. Regarding domain knowledge, only one out of the six analyses concerning search time or the amount of patterns yielded a significant effect. A possible explanation for this result could be the questionable validity of the criteria used to distinguish between known and unknown domains. The difference in knowledge regarding familiar and unfamiliar domains is not as large as had been expected. The notion of end‐users displaying habitual modes of behaviour is given considerable support by the data. This is reflected by the limited number of patterns observed.

This study aims to propose a learning pattern analysis method which can improve a predictive model’s performance, as well as discover hidden insights into micro-level learning pattern. Analyzing student’s learning patterns can help instructors understand how their course design or activities shape learning behaviors; depict students’ beliefs about learning and their motivation; and predict learning performance by analyzing individual students’ learning patterns. Although time-series analysis is one of the most feasible predictive methods for learning pattern analysis, literature-indicated current approaches cannot provide holistic insights about learning patterns for personalized intervention. This study identified at-risk students by micro-level learning pattern analysis and detected pattern types, especially at-risk patterns that existed in the case study. The connections among students’ learning patterns, corresponding self-regulated learning (SRL) strategies and learning performance were finally revealed.

The method used long short-term memory (LSTM)-encoder to process micro-level behavioral patterns for feature extraction and compression, thus the students’ behavior pattern information were saved into encoded series. The encoded time-series data were then used for pattern analysis and performance prediction. Time series clustering were performed to interpret the unique strength of proposed method.

Successful students showed consistent participation levels and balanced behavioral frequency distributions. The successful students also adjusted learning behaviors to meet with course requirements accordingly. The three at-risk patten types showed the low-engagement (R1) the low-interaction (R2) and the non-persistent characteristics (R3). Successful students showed more complete SRL strategies than failed students. Political Science had higher at-risk chances in all three at-risk types. Computer Science, Earth Science and Economics showed higher chances of having R3 students.

The study identified multiple learning patterns which can lead to the at-risk situation. However, more studies are needed to validate whether the same at-risk types can be found in other educational settings. In addition, this case study found the distributions of at-risk types were vary in different subjects. The relationship between subjects and at-risk types is worth further investigation.

This study found the proposed method can effectively extract micro-level behavioral information to generate better prediction outcomes and depict student’s SRL learning strategies in online learning. The authors confirm that the research in their work is original, and that all the data given in the paper are real and authentic. The study has not been submitted to peer review and not has been accepted for publishing in another journal.