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This paper aims to propose a conceptual model for improving the organization of user needs information in the big data environment.

A conceptual model of the organization of user needs information based on Linked Data techniques is constructed. This model has three layers: the Data Layer, the Semantic Layer and the Application Layer.

Requirements for organizing user needs information in the big data environment are identified as follows: improving the intelligence level, establishing standards and guidelines for the description of user needs information, enabling the interconnection of user needs information and considering individual privacy in the organization and analysis of user needs.

This Web of Needs model could be used to improve knowledge services by matching user needs information with increasing semantic knowledge resources more effectively and efficiently in the big data environment.

This study proposes a conceptual model, the Web of Needs model, to organize and interconnect user needs. Compared with existing methods, the Web of Needs model satisfies the requirements for the organization of user needs information in the big data environment with regard to four aspects: providing the basis and conditions for intelligent processing of user needs information, using RDF as a description norm, enabling the interconnection of user needs information and setting various protocols to protect user privacy.

The purpose of this study is to develop a framework for the perceived intelligence of VAs and explore the mechanisms of different dimensions of the perceived intelligence of VAs on users’ exploration intention (UEI) and how these antecedents can collectively result in the highest level of UEI.

An online survey on Amazon Mechanical Turk is employed. The model is tested utilizing the structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) approach from the collected data of VA users (N = 244).

According to the SEM outcomes, perceptual, cognitive, emotional and social intelligence have different mechanisms on UEI. Findings from the fsQCA reinforce the SEM results and provide the configurations that enhanced UEI.

This study extends the conceptual framework of perceived intelligence and enriches the literature on anthropomorphism and users’ exploration. These findings also provide insightful suggestions for practitioners regarding the design of VA products.

The purpose of this paper is to evaluate Google question-answering (QA) quality.

Given the large variety and complexity of Google answer boxes in search result pages, existing evaluation criteria for both search engines and QA systems seemed unsuitable. This study developed an evaluation criteria system for the evaluation of Google QA quality by coding and analyzing search results of questions from a representative question set. The study then evaluated Google’s overall QA quality as well as QA quality across four target types and across six question types, using the newly developed criteria system. ANOVA and Tukey tests were used to compare QA quality among different target types and question types.

It was found that Google provided significantly higher-quality answers to person-related questions than to thing-related, event-related and organization-related questions. Google also provided significantly higher-quality answers to where- questions than to who-, what- and how-questions. The more specific a question is, the higher the QA quality would be.

Suggestions for both search engine users and designers are presented to help enhance user experience and QA quality.

Particularly suitable for search engine QA quality analysis, the newly developed evaluation criteria system expanded and enriched assessment metrics of both search engines and QA systems.

This study considers online searching by health information consumers as a learning process. We focus on search sequences, query reformulation, and conceptual changes.

A qualitative user study (30 participants; three health information seeking tasks) investigated mobile searching behavior. Recorded screen activity, questionnaires, and in-depth personal interview data were collected and analyzed.

(1) Search platform sequences of health information consumers in search as a learning process were exacted and their features were highlighted. (2) Query sequence and reformulation pattern of health information consumers were exacted and discussed. (3) The types and degree of conceptual changes of health consumers were reflected by their query reformulation behavior and differ from different health information search tasks. (4) Characteristics of health consumers' search as learning process were revealed.

(1) A novel perspective of consumer health information studies was proposed by exacting search platform sequence, query sequence and linking them with conceptual changes during the search as learning process. (2) Conceptual changes in the searching as a learning process are regarded as a measure of search outcome in this study, in which terms extracted from queries were used to reflect conceptual changes in consumers' mind. (3) Our findings provide evidences that types of health information seeking tasks do have significant influences on the search as a learning process.

The findings of this study can lead to the fit-to-needs of the search platforms, provide advice for information architecture of search list of search platforms, and guide the design of knowledge graph of health information systems.

Potential relationships between information-seeking behavior and conceptual changes in search as a learning process relative to health information were revealed.

This study aims to explore the influence of topic familiarity on the four stages of college students' learning search process.

This study clarified the effects of topic familiarity on students' learning search process by conducting a simulation experiment based on query formulation, information item selection, information sources and learning output.

The results characterized users' interaction behaviors in increasing topic familiarity through their use of more task descriptions as queries, increased reformulation of queries, construction of more purposeful query formulation, reduced attention to a topic's basic concept content and increased exploration of academic platform contents.

This study proposed three innovative indicators which were proposed to evaluate the effects of topic familiarity on college students' learning search process, and the adopted metrics were useful for observing differences in college students' learning output as their topic familiarity increased. It contributes to the understanding of a user's search process and learning output to support the optimization function of learning-related information search systems and improve their effect on the user's search process for learning.

The purpose of this paper is to investigate health care consumers’ diabetes term usage patterns based on Yahoo!Answers social question and answers (Q&A) forum, identified characteristics and relationships among terms within three pairs of related categories identified from the Q&A log, and revealed users’ diabetes term usage patterns.

The Q&A analysis method allowed first-hand investigation of massive data from health care consumers. Visual term clustering analysis across categories was conducted using a multi-dimensional scaling (MDS) visualization method which provides an intuitive and interactive way to explore and discover term association patterns in a visual environment. Closely related categories were identified and corresponding visual term clustering analyses between categories (Sign & Symptom and Organ & Body Part; Diagnosis and Test; and Diagnosis and Medication) as well at the term level were analyzed.

The findings show that there are close relationships between terms in two related categories. Related terms were grouped and patterns were revealed. All the stress values of the MDS analyses fall below 0.10 and RSQ for each of the combined categories is over 0.90 which indicate the investigated terms were well clustered in the visual analyses.

The study provides a unique research methodology for similar consumer health research studies. The results of this study offer insight into consumer health term use behavior, and enrich existing thesauri and subject heading lists, enhance diabetes-related web sites or portals, and improve effectiveness of internal search engines.

The purpose of this paper is to understand the effect of platform characteristics (i.e. media richness and interactivity) on individual perception (i.e. outcome expectations) and consequent behavioral response (i.e. user participation in online health communities (OHCs)) based on the stimulus-organism-response (S-O-R) model.

This study developed a research model to test the proposed hypotheses, and the proposed model was tested using partial least squares structural equation modeling (PLS-SEM) for which data were collected from 321 users with OHC experience using an online survey.

The empirical results show the following: (1) the three dimensions of media richness significantly affect the three outcome expectations, except that richness of expression has no significant effect on the outcome expectation of health self-management competence. (2) Human-to-human interaction significantly affects the three outcome expectations. Moreover, compared with human-to-human interaction, human-to-system interaction has a stronger impact on the outcome expectation of health self-management competence. (3) The three outcome expectations have a significant influence on user participation in OHCs.

This study extends the understanding about how platform characteristics (i.e. media richness and interactivity) motivate user participation in the context of OHCs. Drawing on the S-O-R model, this study reveals the underlying mechanisms by which media richness and interactivity are associated with outcome expectations and by which outcome expectations is associated with user participation in OHCs. This study enriches the literature on media richness, interactivity, outcome expectations and user participation in OHCs, providing insights for developers and administrators of OHCs.

Against the background of the enormous economic transition China is undertaking, government intervention over corporate behavior is a frequent and, arguably, necessary measure. Among the most serious problems facing China, economically and reputationally, are environmental issues. So, how is the government intervening in the environmental performance of Chinese enterprises? And how are Chinese enterprises responding? These are the questions to be answered in this study.

This paper sampled listed companies on China’s Shanghai and Shenzhen Stock Exchanges. The data were collected from the HeXun corporate social responsibility report, CSMAR and WIND databases. A Tobit model was used to conduct the main 2SLS regression analysis, and the robustness tests followed the propensity score matching method.

The analysis shows that environmental performance is positively related to the government subsidies a company receives. The “Eight-point Regulation of the Centre” crack-down on social corruption introduced in 2012 has weakened rent-seeking overall, but rent-seeking behavior through the cloak of corporate environmental performance has become more serious. As a result, non-polluting and state-owned enterprises are significantly less concerned about their environmental performance, while polluting and private enterprises are more motivated to become good environmental citizens.

This research provides a greater understanding of the drivers behind environmentally-responsible behavior in Chinese companies. These insights can be used by policymakers and environmental regulators to incentivize a more widespread ground-swell of change across the gamut of Chinese business.

Environmental policy and practice informed by research-driven recommendations can not only make valuable contributions to the health and well-being of Chinese society but also, as a significant contributor to climate change, environmental reforms have global benefits.

This study explores the motivations behind rent-seeking associated with environmental investment. The findings expand the research horizon of relevant literature on corporate political rent-seeking and deepen the understandings of the economic consequences of corporate investment into environmental practice. The results provide empirical evidence for the Chinese government to implement environmental regulations based on incentives beyond simple profit-making.

This study aims to explore how a Chinese lesson study (LS) supports a teacher's learning of transforming curriculum reform ideas into classroom practices.

Data analyzed in this study included lesson plans, three videotaped research lessons, three audio-taped group meetings, interviews, the teacher's reflection journals and other related materials. Clarke and Hollingsworth's interconnected model of professional growth (IMPG) was adopted as the theoretical and analytical framework for examining the teacher's learning process.

This study found that teachers can deepen their understanding of innovative curriculum ideas and implement them through an iterative cycle of planning, enactment and reflection in LS. Involving knowledgeable others in the process and reflecting on evidence-based evaluation of students' learning outcomes is crucial for changing the teacher's knowledge, beliefs, attitudes and teaching practices. Theoretical tools, such as learning trajectory, are useful for transforming curriculum reform ideas that teachers find abstract and overly broad into concrete and actionable lesson designs.

Theoretically, this study can broaden our knowledge about how the under-studied mechanism of Chinese LS can contribute to helping teachers to transform reform ideas into classroom practices. Practically, this study provides suggestions for researchers and educators to reflect on and improve the effectiveness of teachers' professional development programs in a reform context.

Research and development (R&D) projects are often pursued through a project portfolio (PP). R&D PPs involve many stakeholders, and without proactive management, their interactions may lead to conflict risks. These conflict risks change dynamically with different stages of the PP life cycle, increasing the challenge of PP risk management. Existing conflict risk research mainly focuses on source identification but lacks risk assessment work. To better manage the stakeholder conflict risks (SCRs) of R&D PPs, this study employs the dynamic Bayesian network (DBN) to construct its dynamic assessment model.

This study constructs a DBN model to assess the SCRs in R&D PP. First, an indicator system of SCRs is constructed from the life cycle perspective. Then, the risk relationships within each R&D PPs life cycle stage are identified via interpretative structural modeling (ISM). The prior and conditional probabilities of risks are obtained by expert judgment and Monte Carlo simulation (MCS). Finally, crucial SCRs at each stage are identified utilizing propagation analysis, and the corresponding risk responses are proposed.

The results of the study identify the crucial risks at each stage. Also, for the crucial risks, this study suggests appropriate risk response strategies to help managers better perform risk response activities.

This study dynamically assesses the stakeholder conflict risks in R&D PPs from a life-cycle perspective, extending the stakeholder risk management research. Meanwhile, the crucial risks are identified at each stage accordingly, providing managerial insights for R&D PPs.