This study tries to give a different perspective of the cloud application through a personal knowledge management perspective and its structure in the cloud computing environment. In recent years, there has been a great hype about cloud computing and different books and literature reviews have classified different types of cloud with various definitions and criteria focusing on mainly three different layers of services being infrastructure, platform and software. Instead, this paper provides a meta-observation over an integrated cloud ecosystem through the knowledge window through which a deeper insight into how the cloud, as an ecosystem, provides services that are not feasible in many conventional knowledge management approaches. Adopting a top-down approach, this study tries to illustrate the implications of the cloud at the personal levels from a knowledge-oriented perspective.

Purpose: The purpose of this paper is to look into the impact of important social motives on knowledge‐sharing behavior in a collaborative environment and how people might react to each of these motives.Design/methodology/approach: The applied methodology used in this paper is based on an online survey and statistical hypothesis developed for each of the motives with different comparison means under two circumstances.Findings: The paper exhibits the different impact of each of the motives over the sharing behaviour in collaborative platforms. Despite general beliefs, although many motives might not have a very significant impact under normal behavioral circumstances that is based on either an agreement or disagreement level, all of those tested motives show a positive tendency when tested on the neutral (Dilemma) position.Research limitations/implications: This study has examined a small and generalized group of people for social networking analysis with a limited number of motivational factors tested. Future research can extend the findings on a greater scale and/or examination of more diverse motives.Originality/value: This study tries to change the presumptions about the collaboration‐sharing behaviours and motives and how social environment can behave differently under the same circumstances on a business‐oriented platform. The study also suggests how the findings can be applied to leverage social collaboration more efficiently to enhance knowledge sharing for business purposes.

In modeling of consumer preferences based on online comments, nonlinearity and fuzziness exist in the relationship between the product design attribute and the consumer preference. On the other hand, consumer preferences are not static and changing over the time. Previous studies have proposed few approaches to model the variational consumer preferences based on online comments. However, the obtained models have black box problems and are not easy to be understood by humans as explicit models cannot be shown, which give rise to the research area of explainable artificial intelligence. Therefore, it is necessary to develop understandable and accurate consumer preferences’ models. In this article, a nonlinear time series fuzzy regression method is proposed to model the variational consumer preference based on online comments, which can generate a fuzzy dynamic consumer preference model with interactive terms, second-order and/or higher order terms. The datasets are first extracted from online comments using the sentiment analysis. Then, the polynomial structure of the fuzzy dynamic consumer preference model is established by using multiobjective chaos optimization algorithm. Then, the fuzzy regression method is used to allocate the fuzzy coefficients of each item of the model. Using sweeping robot as a case study, the validation results from the proposed approach are compared with those from fuzzy least squares regression, time series fuzzy least squares regression, fuzzy regression, and time series fuzzy regression, and it is found that the proposed approach performs better than the other four approaches in terms of mean relative errors and mean system credibility.

In online sales platforms, product design attributes influence consumer preferences, and consumer preferences also have a significant impact on future product design optimization and iteration. Online review data are the most intuitive feedback from consumers on products. Using the value of online review information to explore consumer preferences is the key to optimize the products, improve consumer satisfaction and meet consumer requirements. Therefore, the study of consumer preferences based on online reviews is of great importance. However, in previous research on consumer preferences based on online reviews, few studies have modeled consumer preferences. The models often suffer from the nonlinear structure and the fuzzy coefficients, making it challenging to build explicit models. Therefore, this study adopts a fuzzy regression approach with a nonlinear structure to model consumer preferences based on online reviews to provide reference and insight for subsequent studies. First, smartwatches were selected as the research object, and the sentiment scores of product reviews under different topics were obtained by text mining on the product online data. Second, a polynomial structure between product attributes and consumer preferences was generated to investigate the association between them further. Afterward, based on the existing polynomial structure, the fuzzy coefficients of each item in the structure were determined by the fuzzy regression approach. Finally, the mean relative error and mean systematic confidence of the fuzzy regression with nonlinear structure method were numerically calculated and compared with fuzzy least squares regression, fuzzy regression, adaptive neuro fuzzy inference system (ANFIS) and K-means-based ANFIS, and it was found that the proposed method was relatively more effective in modeling consumer preferences.

Purpose: – The purpose of this paper is to introduce a new knowledge quality assessment framework based on interdependencies between content and schema as knowledge resources to enhance the quality of the knowledge that is being generated, disseminated and stored in a collaborative environment.Design/methodology/approach: – A knowledge elaboration approach is based on intervening factors of schematic clustering applied to a trial wiki bulletin board. Through this schematic intervention in the form of group creation within a wiki environment, a user-centric mechanism is created to substantiate, compose and narrate the generated contents in a self-organizing way.Findings: – Through this approach, quality in content can be enhanced by means of a favourably manipulated collaboration schema adopted by the knowledge management system (KMS) users instead of applying knowledge mining tools.Research limitations/implications: – With consideration to trust as a significant factor in this study, the verification and referral process may vary for KMS structures that are of larger scale or in low-trust collaborative environments.Originality/value: – This study demonstrates transition to higher quality knowledge with less time spent on the original content refinement and composition by paying due consideration to the interdependencies between knowledge resource content and its schema. Validation is done via a clustered group structure in a specially designed wiki which had been used as a discussion bulletin board on directed topics over an extended period.

In previous research on the development of the relationships between product attributes and customer satisfaction, the models did not adequately consider nonlinearity and the fuzzy emotions of customers in online reviews. Also, stable customer satisfaction was considered. However, customer satisfaction is changing with time rapidly, and a time-series analysis for customer satisfaction has not been conducted previously. To address these challenges, this study designed a novel methodology using adaptive neuro-fuzzy inference systems (ANFIS) in conjunction with Bi-objective particle swarm optimization (BOPSO) and sentiment analysis techniques. Sentiment analysis is employed to extract time-series customer satisfaction data from online reviews. Then, an ANFIS with the BOPSO method is proposed for the establishment of customer satisfaction models. In previous studies, ANFIS is an effective method to model customer satisfaction which can handle fuzziness and nonlinearity. However, when dealing with a large number of inputs, the modeling process may fail due to the complexity of the structure and the lengthy computational time required. Incorporating the BOPSO algorithm into ANFIS can identify the optimal inputs in ANFIS and effectively mitigate the inherent limitations of ANFIS. Using mobile phones as a case study, a comparison was performed between the proposed approach and another four approaches in modeling time-series customer satisfaction.