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--- - |2 Abstract Bayesian methods incorporate model parameter information prior to data collection. Eliciting information from content experts is an option, but has seen little implementation in Bayesian item response theory (IRT) modeling. This study aims to use ethical reasoning content experts to elicit prior information and incorporate this information into Markov Chain Monte Carlo (MCMC) estimation. A six‐step elicitation approach is followed, with relevant details at each stage for two IRT items parameters: difficulty and guessing. Results indicate that using content experts is the preferred approach, rather than noninformative priors, for both parameter types. The use of a noninformative prior for small samples provided dramatically different results when compared to results from content expert–elicited priors. The WAMBS (When to worry and how to Avoid the Misuse of Bayesian Statistics) checklist is used to aid in comparisons. - Journal of Educational Measurement, Volume 55, Issue 3, Page 373-402, Fall 2018.

--- - |2 Abstract This study investigates the effect of multidimensionality on extraction of latent classes in mixture Rasch models. In this study, two‐dimensional data were generated under varying conditions. The two‐dimensional data sets were analyzed with one‐ to five‐class mixture Rasch models. Results of the simulation study indicate the mixture Rasch model tended to extract more latent classes than the number of dimensions simulated, particularly when the multidimensional structure of the data was more complex. In addition, the number of extracted latent classes decreased as the dimensions were more highly correlated regardless of multidimensional structure. An analysis of the empirical multidimensional data also shows that the number of latent classes extracted by the mixture Rasch model is larger than the number of dimensions measured by the test. - Journal of Educational Measurement, Volume 55, Issue 3, Page 403-420, Fall 2018.

--- - |2 Abstract Person reliability parameters (PRPs) model temporary changes in individuals’ attribute level perceptions when responding to self‐report items (higher levels of PRPs represent less fluctuation). PRPs could be useful in measuring careless responding and traitedness. However, it is unclear how well current procedures for estimating PRPs can recover parameter estimates. This study assesses these procedures in terms of mean error (ME), average absolute difference (AAD), and reliability using simulated data with known values. Several prior distributions for PRPs were compared across a number of conditions. Overall, our results revealed little differences between using the χ or lognormal distributions as priors for estimated PRPs. Both distributions produced estimates with reasonable levels of ME; however, the AAD of the estimates was high. AAD did improve slightly as the number of items increased, suggesting that increasing the number of items would ameliorate this problem. Similarly, a larger number of items were necessary to produce reasonable levels of reliability. Based on our results, several conclusions are drawn and implications for future research are discussed. - Journal of Educational Measurement, Volume 55, Issue 3, Page 421-432, Fall 2018.

--- - |2 Abstract This research derived information functions and proposed new scalar information indices to examine the quality of multidimensional forced choice (MFC) items based on the RANK model. We also explored how GGUM‐RANK information, latent trait recovery, and reliability varied across three MFC formats: pairs (two response alternatives), triplets (three alternatives), and tetrads (four alternatives). As expected, tetrad and triplet measures provided substantially more information than pairs, and MFC items composed of statements with high discrimination parameters were most informative. The methods and findings of this study will help practitioners to construct better MFC items, make informed projections about reliability with different MFC formats, and facilitate the development of MFC triplet‐ and tetrad‐based computerized adaptive tests. - Journal of Educational Measurement, Volume 55, Issue 3, Page 357-372, Fall 2018.

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This article examines the “chicken‐egg” two‐way relationships between online consumer reviews and sales, and assesses the dual influencer and indicator roles of online consumer reviews in relation to purchase behavior. Considering the time factor, we adopt the methodology of Granger causality test and track 3,390 products on Amazon.com over a 2‐month period. The results reveal that a causality loop exists between online consumer review volume and sales. Specifically, our findings indicate that the volume of negative consumer reviews drive consumers' purchasing decisions, but the volume of positive consumers reviews only marginally affects purchasing decisions. Also, consumers generate more positive reviews than negative reviews after sales. Our results highlight the importance of negative consumer reviews; negative reviews not only lead to sales, but sales, in turn, lead to higher volume of negative reviews. The findings suggest an alternative strategy for practitioners to address negative online consumer reviews and highlight the awareness effect of online consumer review postings that can later convert into purchase behaviors.

This representative study of German search engine users (N = 1,000) focuses on the ability of users to distinguish between organic results and advertisements on Google results pages. We combine questions about Google's business with task‐based studies in which users were asked to distinguish between ads and organic results in screenshots of results pages. We find that only a small percentage of users can reliably distinguish between ads and organic results, and that user knowledge of Google's business model is very limited. We conclude that ads are insufficiently labelled as such, and that many users may click on ads assuming that they are selecting organic results.

Objective The objective of this literature review was to summarise current research regarding how consumers seek health‐related information from social media. Primarily, we hope to reveal characteristics of existing studies investigating the health topics that consumers have discussed in social media, ascertaining the roles social media have played in consumers’ information‐seeking processes and discussing the potential benefits and concerns of accessing consumer health information in social media. Methods The Web of Science Core Collection database was searched for existing literature on consumer health information seeking in social media. The search returned 214 articles, of which 21 met the eligibility criteria following review of full‐text documents. Conclusion Between 2011 and 2016, twenty‐one studies published explored various topics related to consumer information seeking in social media. These ranged from online discussions on specific diseases (e.g. diabetes) to public health concerns (e.g. pesticide residues). Consumers’ information needs vary depending on the health issues of interest. Benefits of health seeking on social media, in addition to filling a need for health information, include the social and emotional support health consumers gain from peer‐to‐peer interactions. These benefits, however, are tempered by concerns of information quality and authority and lead to decreased consumer engagement.

This work extends the masspersonal communication model (MPCM; O'Sullivan & Carr, 2017) by introducing anticipated interaction as a way to understand variations within the masspersonal continuum. Drawing from Thompson's mediated communication framework (1995), we argue that anticipated interaction paves the way for establishing a communicative relationship between interactants. In social media, this relationship is rooted in a sender's expectations for audience response and the imagined responsive audience. Using experience sampling, we show that anticipated interaction varies across social media. Further, we outline the relational and situational factors associated with expecting response and the specificity of imagined responsive audience. These variations and their sources characterize masspersonal communication as a socially and technologically situated practice shaped by multiple intersecting influences.

This research focuses on the identification of social roles and an investigation of their influence in online context. Relying on a systemic approach for role conceptualization, we investigate member's activity, shared content and position in the network within a consumer to consumer social media‐based community (SMC) around a product. This investigation led to the identification of ten core roles, based on three key elements: object of interest (product, practice, and community), main contribution type (sharing information and seeking information), individual orientation (factual, emotional). We propose an explanation about how these roles, through their positioning, participate in the community dynamics and how they contribute to the creation and diffusion of cookery as a social practice, shaping the periphery around this practice.

Political information sharing in social media offers citizens opportunities to engage with news and express their political views, but how do different patterns of online political information exposure, including both incidental and selective exposure, affect sharing? Using two‐wave panel survey data collected in the United States, we examine the relationship between incidental and selective exposure and their consequent links to political information sharing, across different levels of strength of political party affiliation. Our results demonstrate that incidental exposure to counter‐attitudinal information drives stronger partisans to more actively seek out like‐minded political content, which subsequently encourages political information sharing on social media. The results highlight the need to consider both types of political information exposure when modeling citizens' political behavior online.

The explosive growth of mobile apps makes it difficult for users to find their needed apps in a crowded market. An effective mechanism that provides high quality app recommendations becomes necessary. However, existing recommendation techniques tend to recommend similar items but fail to consider users’ functional requirements, making them not effective in the app domain. In this article, we propose a recommendation architecture that can generate app recommendations at the functionality level. We address the redundant recommendation problem in the app domain by highlighting users’ functional requirements, an element that has received scant attention from existing recommendation research. Another main feature of our work is extracting app functionalities from textural user reviews for recommendation. We also propose an effective approach for functionality extraction. Experiments conducted on a real‐world dataset show that our proposed AppRank method outperforms other commonly used recommendation methods. In particular, it doubles the recall value of the second best method under an extremely sparse setting, increases the overall ranking accuracy of the second best method by 14.27%, and retains a high diversity of 0.99.

For diversity and novelty evaluation in information retrieval, we expect that the novel documents are always ranked higher than the redundant ones and the relevant ones higher than the irrelevant ones. We also expect that the level of novelty and relevancy should be acknowledged. Accordingly, we expect that the evaluation algorithm would reward rankings that respect these expectations. Nevertheless, there are few research articles in the literature that study how to meet such expectations, even fewer in the field of biomedical information retrieval. In this article, we propose a new metric for novelty and relevancy evaluation in biomedical information retrieval based on an aspect‐level performance measure introduced by TREC Genomics Track with formal results to show that those expectations above can be respected under ideal conditions. The empirical evaluation indicates that the proposed metric, geNov, is greatly sensitive to the desired characteristics above, and the three parameters are highly tuneable for different evaluation preferences. By experimentally comparing with state‐of‐the‐art metrics for novelty and diversity, the proposed metric shows its advantages in recognizing the ranking quality in terms of novelty, redundancy, relevancy, and irrelevancy and in its discriminative power. Experiments reveal the proposed metric is faster to compute than state‐of‐the‐art metrics.

Journal of Knowledge Management, Volume 21, Issue 5, Page 1013-1034, September 2017.
Purpose The aim of this paper is to frame the stakeholder-driven system mapping approach in the context of climate change, building on stakeholder knowledge of system boundaries, key elements and interactions within a system and to introduce a decision support tool for managing and visualising this knowledge into insightful system maps with policy implications. Design/methodology/approach This methodological framework is based on the concepts of market maps. The process of eliciting and visualising expert knowledge is facilitated by means of a reference implementation in MATLAB, which allows for designing technological innovation systems models in either a structured or a visual format. Findings System mapping can contribute to evaluating systems for climate change by capturing knowledge of expert groups with regard to the dynamic interrelations between climate policy strategies and other system components, which may promote or hinder the desired transition to low carbon societies. Research limitations/implications This study explores how system mapping addresses gaps in analytical tools and complements the systems of innovation framework. Knowledge elicitation, however, must be facilitated and build upon a structured framework such as technological innovation systems. Practical implications This approach can provide policymakers with significant insight into the strengths and weaknesses of current policy frameworks based on tacit knowledge embedded in stakeholders. Social implications The developed methodological framework aims to include societal groups in the climate policy-making process by acknowledging stakeholders’ role in developing transition pathways. The system map codifies stakeholder input in a structured and transparent manner. Originality/value This is the first study that clearly defines the system mapping approach in the frame of climate policy and introduces the first dedicated software option for researchers and decision makers to use for implementing this methodology.

Journal of Knowledge Management, Volume 21, Issue 5, Page 1035-1052, September 2017.
Purpose The purpose of this paper is to explore and characterize knowledge integration approaches for integrating external knowledge of suppliers into new product development projects. Design/methodology/approach This paper is based on a multiple, in-depth case study of six product development projects at three knowledge-intensive manufacturing firms. Findings Firms make purposeful choices to devise knowledge integration approaches when working in collaborative buyer – supplier projects. The knowledge characteristics of the supplier input guide the choice of either coupling knowledge sharing and combining across firms or decoupling knowledge sharing (across firms) and knowledge combining (within firms). Research limitations/implications This study relies on a limited number of case studies and considers only one supplier relationship in each project. Further studies could examine the challenge of knowledge integration in buyer – supplier relationships in different contexts, i.e. in relation to innovation complexity and uncertainty. Practical implications Managers need to make choices when designing knowledge integration approaches in collaborative product development projects. The use of coupled and decoupled approaches can help balance requirements in terms of joint problem-solving across firms, the efficiency of knowledge integration and the risks of knowledge leakage. Originality/value The conceptualization of knowledge integration as knowledge sharing and knowledge combining extends existing perspectives on knowledge integration as either a transfer of knowledge or as revealing the presence of pertinent knowledge without entirely transmitting it. The findings point to the complexity of knowledge integration as a process influenced by knowledge characteristics, perspectives on knowledge, openness of firm boundaries and elements of knowledge sharing and combining.

Journal of Knowledge Management, Volume 21, Issue 5, Page 1053-1076, September 2017.
Purpose Although mobile devices are ubiquitous among employees, their awareness and readiness to use mobile technologies for competence development is still not widespread and therefore requires further exploration. The purpose of this study is to propose a conceptual model based on the unified theory of acceptance and use of technology (UTAUT) to explain the determinants that affect employees’ intention to use mobile devices and software for knowledge transfer during the process of knowledge management. Design/methodology/approach A conceptual model based on the UTAUT with new variables concerning relative usability (RU) and user autonomy (UA) and new connections between the determinants was developed as a result of a subject matter literature review. A structural equation modelling approach was used to validate the model on the basis of data collected via a survey collected from 371 employees from 21 sectors, both public and private. Findings The UTAUT model extended by new variables like RU and UA explains employee acceptance of mobile technologies for knowledge transfer reasonably well. New proposed variables highlighted that the usability of technology compared to other solutions and user autonomy in the selection and the use of applications have the strongest impact on the employees’ intention to use mobile devices and software for knowledge transfer. Research limitations/implications This model explains the 55 per cent behavioral intention of employees to use mobile technologies for knowledge transfer. Even though it is quite high in terms of acceptance theories, some new variables should be explored. Furthermore, study does not verify whether m-learning acceptance for knowledge transfer is sector-specific. Practical implications Mobile technologies used for knowledge transfer by employees should allow for high UA through their ability to select solutions that they find convenient, use of preferred platforms, personalize applications and utilize devices and software in various environments. They should not be simplified and should have the same functionality and efficiency of use as alternative solutions like web and desktop applications, even if additional effort to learn them would be required. Mobile technologies that take into account UA and RU support the process of employees capturing, distributing and effectively using knowledge. Originality/value The elaborated model provides a valuable solution with practical implications for increasing mobile technologies acceptance for knowledge transfer. The study results contribute both to knowledge management and technology acceptance research fields by introducing two new determinants for the acceptance of technologies in knowledge transfer, such as UA and RU with several additional connections between existing UTAUT variables.

Journal of Knowledge Management, Volume 21, Issue 5, Page 1077-1097, September 2017.
Purpose This study aims to investigate how psychological motivations influence the knowledge-sharing intention of employees and how these effects are moderated by traditional Chinese culture. Design/methodology/approach A questionnaire survey was conducted in China to test the research model. The target samples comprised MBA students who have enough practical experience, as well as considerable academic experience. Findings The results suggest that anticipated reciprocal relationships, anticipated extrinsic rewards and sense of self-worth exert different effects on knowledge-sharing intention. In addition, traditional Chinese culture plays different roles in the relationships among these three psychological motivations and knowledge-sharing intention. Specifically, guanxi orientation positively moderates the effect of anticipated reciprocal relationships and negatively moderates the effect of sense of self-worth. Face gaining negatively moderates the effect of anticipated reciprocal relationships and positively moderates the effect of sense of self-worth. Face saving negatively moderates the effect of anticipated reciprocal relationships and sense of self-worth. Originality/value A few studies in extant knowledge management (KM) literature provided insights into how traditional Chinese culture could directly affect knowledge sharing. The authors depart from these studies by integrating these characteristic indigenous concepts (i.e. face and guanxi orientation) into this study. The authors offer an indigenous cultural view of how these indigenous concepts truly influence an individual’s psychological states and inclination in KM literature. Through this approach, the results confirm that these cultural factors do play an important role during the formation of knowledge-sharing intention and reveals several important research findings.

Journal of Knowledge Management, Volume 21, Issue 5, Page 1098-1119, September 2017.
Purpose The aim of this study is to empirically test the link between employee ambidexterity and two supportive organizational cultures, namely, a perceived culture of empowerment and a knowledge-sharing culture. Furthermore, the paper addresses the mechanisms through which these supportive organizational cultures work to enable employees to engage in ambidextrous behaviour. Specifically, the role of intrinsic motivation is investigated. Design/methodology/approach Data were obtained from 136 managers employed in the five main Belgian service sectors. Findings The findings indicate that a perceived culture of empowerment is positively related to intrinsic motivation, which in turn facilitates employee ambidexterity. Also, a moderating effect of extrinsic motivation on employee ambidextrous behaviour is found. At the same time, a perceived knowledge-sharing culture is having no effect on ambidexterity or intrinsic motivation. Research limitations/implications Insights into the roles of individuals in achieving ambidexterity help to advance the theoretical understanding of how a supportive organizational context may enhance employee ambidexterity. Originality/value Despite the growing body of research on antecedents of ambidexterity, there is still lack of thorough understanding of how a supportive organizational context may enhance employee ambidexterity and the roles of individuals in achieving ambidexterity. This is one of the first studies that investigate these factors in relation to individual level ambidexterity (as opposed to organization level ambidexterity).

Journal of Knowledge Management, Volume 21, Issue 5, Page 1120-1141, September 2017.
Purpose This paper was aimed at investigating the impact of knowledge management (KM) procedures on enriching human resources in Water and Waste Water Company in East Azerbaijan, Iran. Design/methodology/approach The samples used in this study included the employees of Water and Waste Water Company in Tabriz, East Azerbaijan, Iran. A questionnaire was used for collecting data from the employees of the abovementioned company. Its reliability and validity were first examined and checked. Then, Smart partial least squares 2.0 was used for analyzing the structural model. Findings The results acknowledged the validity of the introduced model for enriching human resources. The findings indicated that five variables, namely, kind of knowledge, top managers, information technology, culture and organization of knowledge, have significant impact on enriching human resources. Research limitations/implications It is undoubtable that research studies might have specific limitations which should be pointed out and addressed in future studies. The followings are the major limitations of the study: because the present study was carried out in Water and Wastewater Company in Tabriz, East Azerbaijan, hence, generalizing the findings of this study to other professional contexts and organizations should be made with caution. In fact, the present study need to be replicated in other context to find whether the same or different results are obtained. In other words, different cultural, contextual and professional conditions might result differently from the ones reported here. Inasmuch as the present study was a cross-sectional study and the data were collected via questionnaire, a longitudinal study with a longer observation and investigation might shed more light on the efficacy of KMS in organizations. The present study focused on a specific dependent variable (human resource empowerment) which was explained by different dimensions of an independent variable, i.e. KM. However, the impact and efficacy of KMS can be investigated on other organizational variables and parameters. In this study, due to certain logistic and real-life limitations such as the limited time of the staff members of the target organization, we had to use only one data-collection instrument. Nevertheless, future studies can use data triangulation so as to better capture professional contexts of the study. Furthermore, another limitation of the study is related to the individual variables of the employees which remained untouched. That is, whether employees’ personal, emotional and cognitive variables can modify the impact of KM on human resource empowerment was not investigated in the present study. Practical implications Organizational managers are recommended to provide the background for employees to share their experiences. Organizations should invest on designing and developing patterns and strategic perspectives in human resource empowerment as a key factor toward becoming knowledge-based organizations. Originality/value This paper is one of the few applied studies which acknowledged the relationship between knowledge management initiatives and empowering human resources. It addresses the gap between knowledge management and human resource empowerment.

Journal of Knowledge Management, Volume 21, Issue 5, Page 1142-1162, September 2017.
Purpose Although most of the literature supports the existence of a substitutive relationship between exploration and exploitation, some authors suggest that this relationship is complementary (ambidexterity), and others argue that there is no relationship. This paper aims to introduce organizational innovation into the analysis and discusses which of these three relationships prevails. Design/methodology/approach Analyses were performed using data from Spanish Technological Innovation Panel for the period 2008-2013. It should be emphasized that the use of panel data is essential in the analysis of the interaction of exploration and exploitation, as exploration only makes sense in the long run. Econometric strategy uses a two-stage selection model, estimated using the Wooldridge’s (1995) consistent estimator for panel data with sample selection. To perform the test, the hypothesis uses the approach of complementarity. Findings The results show that the relationships exploration-organizational innovation and exploitation-organizational innovation are complementary, provided that the analysis is performed on companies that simultaneously carry out exploration and exploitation activities, respectively. This indicates that the achievement of ambidexterity is strongly conditioned by the simultaneous realization of organizational innovations. Practical implications Managers and policymakers should be aware that the simultaneous implementation of exploration and exploitation yields better results when the corresponding organizational innovations are also implemented. Originality/value This paper extends the empirical investigation of the relationship between exploration and exploitation, seen in conjunction with organizational innovation, and using the complementarity approach as a research tool.

Journal of Knowledge Management, Volume 21, Issue 5, Page 1163-1182, September 2017.
Purpose In downturns, social enterprises (SEs) arise as a resilience business model. The authors focus on knowledge absorptive capacity (KAC) as a key dynamic capability to create shared value (SHV). The purpose of this study is to analyze whether it exists a cause-effect relationship between KAC and SHV creation in SEs. Design/methodology/approach The research model relates KAC and SHV creation. SHV creation involves two components: economic value (EV) and social value (SV). The authors argue that KAC has a positive effect on EV and SV creation, considering SV as a mediator variable. The model is tested by means of PLS-SEM. Findings The results reveal the existence of a positive and significant relationship between KAC and SHV creation, as well as the mediating role of SV creation. Research limitations/implications The sample comprises 127 SEs from the Euro-Mediterranean region (euro-region comprising areas from France and Spain), it would be interesting to include other euro-regions in future research. Practical implications The authors confirm KAC as an important antecedent of SHV creation and also that SEs create EV by means of SV, pointing to the existence of direct and indirect effects. This way, confirming the existence of a mediating effect. Originality/value It points the importance of KAC in SEs, something that has not been addressed before by the literature. It also goes beyond performance by considering SHV creation as a measure of SEs’ outcomes, as well as the definition of the variable SHV including not only social related issues but also environmental (green) issues.

Journal of Knowledge Management, Volume 21, Issue 5, Page 1183-1201, September 2017.
Purpose The purpose of this paper is to discuss how the management of collective intellectual capital (CIC) occurs in a seaport through the actions of the network coordinator. Design/methodology/approach A case study was conducted in a seaport, focusing on the actions taken by a network coordinator – a port authority – to develop the seaport’s CIC. The seaport is conceptualised as a meta-organisation, composed by interdependent actors which may possess different interests and different levels of power. Findings Evidence suggests that the mobilisation of different dimensions of power, in both coercive and non-coercive ways, is needed to promote a higher level of collaboration. Indeed, by mobilising non-coercive dimensions of power, the network coordinator can foster a sense of community within the meta-organisation, grounded in a trust-based collective culture that can potentiate collaboration, and thus allow the attainment of a more “sustainable” type of CIC. Research limitations/implications Despite the validity of the interpretations provided by the case study, generalisation of this study should only be conducted in a theoretically framed manner. Practical implications The findings can provide network coordinators with a better understanding of the consequences of using different dimensions of power to leverage its intangible assets and enhance the meta-organisation’s performance. Originality/value The paper focus on the IC management of a specific type of meso-level unit, which possess some particular characteristics of its own: a seaport. Also, the paper aims to fill a gap in literature regarding the management of different dimensions of power and its effects over IC creation.

Journal of Knowledge Management, Volume 21, Issue 5, Page 1202-1217, September 2017.
Purpose The purpose of this paper is to empirically examine the moderating effect of competitive advantage on the resource value and rareness combination and firm performance. Design/methodology/approach The authors adopted a quantitative research method to achieve the aims of this study. The data collected came from a sample of footwear firms and the hypotheses assessed by multivariate analysis. Findings The findings provide insights into previously inconsistent findings regarding the exploitation of resources and capabilities and contribute to resolving these issues by considering the conditions under which the resource value and rareness combination contributes to firm performance levels and the effects of competitive advantage on this relationship. Interestingly, the authors find that it cannot be inferred that the rarer the resources and capabilities combination, the greater the probability of attaining competitive advantage. Research limitations/implications As limitations, the authors would highlight the fact that the results are specific to one particular industrial sector, and thus not susceptible to the drawing of generalisations as well as the fact that the study does not make recourse to triangulating its methodology (for example, through interviews), which would generate more precise, detailed and objective information. The findings have important managerial implications and the authors close by presenting future research directions. Originality/value This study offers an explanation for previous mixed findings on the relationship between the resource value and rareness combination and firm performance and makes a contribution regarding how such combinations are of great relevance to the organisation and business strategy to turn in better performance levels.

Journal of Knowledge Management, Volume 21, Issue 5, Page 1218-1239, September 2017.
Purpose The purpose of this paper is to identify the factors influencing the growth of healthcare virtual communities of practice (VCoPs) through a seven-year longitudinal study conducted using metrics from social-network and semantic analysis. By studying online communication along the three dimensions of social interactions (connectivity, interactivity and language use), the authors aim to provide VCoP managers with valuable insights to improve the success of their communities. Design/methodology/approach Communications over a period of seven years (April 2008 to April 2015) and between 14,000 members of 16 different healthcare VCoPs coexisting on the same web platform were analysed. Multilevel regression models were used to reveal the main determinants of community growth over time. Independent variables were derived from social network and semantic analysis measures. Findings Results show that structural and content-based variables predict the growth of the community. Progressively, more people will join a community if its structure is more centralised, leaders are more dynamic (they rotate more) and the language used in the posts is less complex. Research limitations/implications The available data set included one Web platform and a limited number of control variables. To consolidate the findings of the present study, the experiment should be replicated on other healthcare VCoPs. Originality/value The study provides useful recommendations for setting up and nurturing the growth of professional communities, considering, at the same time, the interaction patterns among the community members, the dynamic evolution of these interactions and the use of language. New analytical tools are presented, together with the use of innovative interaction metrics, that can significantly influence community growth, such as rotating leadership.

Journal of Knowledge Management, Volume 21, Issue 5, Page 1240-1253, September 2017.
Purpose The purpose of this paper is to investigate the impacts of absorptive capacity, learning motivation and acquired knowledge on knowledge transfer from business schools to business organizations, as it has been realized that in-service training business students can serve as a channel as well as a source in this type of knowledge transfer. The study also examines the mixed moderating role of job autonomy in the relationship between acquired knowledge and knowledge transfer. Design/methodology/approach A sample of 344 in-service training business students in Vietnam was surveyed to collect data. Confirmatory factor analysis was used to validate the measures, and structural equation modeling was used to test the hypotheses. Findings The findings reveal that absorptive capacity positively affects acquired knowledge but it does not have an effect on knowledge transfer. Learning motivation, however, has positive effects on both acquired knowledge and knowledge transfer. The findings also indicate that acquired knowledge is a determinant of knowledge transfer. Finally, job autonomy plays the role of a mixed moderator in the relationship between acquired knowledge and knowledge transfer. Practical implications This study signals the participating parties –business schools, business organizations and in-service training business students –that absorptive capacity, learning motivation, acquired knowledge and job autonomy are critical to the transfer of knowledge from business schools to business organizations. Originality/value This study contributes to the literature on university-to-industry knowledge transfer by providing empirical evidence for key determinants of knowledge transfer from business schools to business organizations through a new channel of knowledge transfer – in-service training business students.

Journal of Knowledge Management, Volume 21, Issue 5, Page 1254-1271, September 2017.
Purpose The purpose of this paper is to construct and investigate relationships between knowledge-sharing factors, attitude and the intention to share of UK academics, as research on knowledge sharing in higher education is extremely sparse. Design/methodology/approach A research model and hypotheses were constructed from individual and organisational factors that were identified to affect knowledge sharing. Questionnaire data were obtained from 367 academics concerning their attitude and intention towards knowledge sharing. This was then used in a two-stage structural equation modelling approach where the measurement model was used for confirmatory factor analysis. The structural model was used to measure and test the hypothesised relationships. Findings Findings indicate that, in general, individual beliefs amongst academics were more influential on their knowledge-sharing attitudes than organisational culture. Furthermore, leadership was the most influential factor within the overall organisational culture whereas autonomy demonstrated the weakest relationship. Belief in the possibility of rewards through associations was found to be a highly significant individual factor. The relationship between attitude and intention was relatively weak although still statistically significant. Originality/value The research demonstrates that management should ensure that departmental leaders promote knowledge sharing and that valued rewards are linked to sharing within the department.

Journal of Knowledge Management, Volume 21, Issue 5, Page 1272-1291, September 2017.
Purpose The purpose of this paper is to analyze the issue of knowledge transfer in the context of international asymmetric alliances. The objective is mainly to identify the barriers that can impede the knowledge transfer between asymmetric partners and to analyze the solutions adopted to overcome these barriers. Design/methodology/approach The authors use a qualitative study involving six cases of asymmetric alliances between Tunisian small-and-medium enterprises (SMEs) and European multinational corporations (MNCs). Findings The results of this research highlight a set of obstacles related to the context of asymmetric alliance itself and the nature of knowledge transferred by partners. The study emphasizes the importance of translation using artefacts by both partners and proximity with its geographical, organizational and technological dimensions to overcome these obstacles. Research limitations/implications The investigation of knowledge transfer in asymmetric alliances was based on the role played by only three proximity dimensions (geographical, organizational and technological), while other factors, such as institutional, social and cultural issues, were not considered. Indeed, future research may take these variables into account in studying solutions to overcome knowledge-transfer barriers in asymmetric alliances. Practical implications The paper calls the attention of asymmetric alliance managers to the importance of translation to perform work processes, facilitate knowledge transfer and overcome linguistic barriers. Managers should use virtual artefacts to reduce the constraints resulting from their geographical remoteness and to strengthen cooperation. Further, reinforcing geographical, organizational and technological proximity between partners involved in an international alliance is essential to facilitate knowledge transfer, essentially of tacit knowledge, and to accelerate innovation. Originality/value This study emphasizes the importance of the simultaneous role of artefacts, translation and proximity in overcoming obstacles related to the asymmetric alliance itself and the nature of knowledge transferred by partners. The results shed light on the issue of knowledge generation in asymmetric alliances.

Decomposing a document written by more than one author into sentences based on authorship is of great significance due to the increasing demand for plagiarism detection, forensic analysis, civil law (i.e., disputed copyright issues), and intelligence issues that involve disputed anonymous documents. Among existing studies for document decomposition, some were limited by specific languages, according to topics or restricted to a document of two authors, and their accuracies have big room for improvement. In this paper, we consider the contextual correlation hidden among sentences and propose an algorithm for Sequential and Unsupervised Decomposition of a Multi‐Author Document (SUDMAD) written in any language, disregarding topics, through the construction of a Hidden Markov Model (HMM) reflecting the authors' writing styles. To build and learn such a model, an unsupervised, statistical approach is first proposed to estimate the initial values of HMM parameters of a preliminary model, which does not require the availability of any information of author's or document's context other than how many authors contributed to writing the document. To further boost the performance of this approach, a boosted HMM learning procedure is proposed next, where the initial classification results are used to create labeled training data to learn a more accurate HMM. Moreover, the contextual relationship among sentences is further utilized to refine the classification results. Our proposed approach is empirically evaluated on three benchmark datasets that are widely used for authorship analysis of documents. Comparisons with recent state‐of‐the‐art approaches are also presented to demonstrate the significance of our new ideas and the superior performance of our approach.

We examine the role of attributions, the seriousness of wrongdoing, and emotion in shaping individuals' whistle‐blowing intentions in the context of health information privacy violations. Based on 3 studies in which the intentionality of wrongdoing and the stability of wrongdoing were manipulated independently, we found consistent evidence that the intentionality of wrongdoing affects anticipated regret about remaining silent. The findings regarding the effect of stability, however, were mixed. In study 1, the stability of wrongdoing was found to affect anticipated regret about remaining silent, and in studies 2 and 3, stability was found to have a direct effect on whistle‐blowing intention but no effect on anticipated regret about remaining silent. In the 3 studies, the seriousness of wrongdoing was found to have an effect on whistle‐blowing intentions, but this effect was mediated by anticipated regret about remaining silent. Implications for research and practice are discussed.

In recent years, social media has become a significant resource for improving healthcare and mental health. Mental health forums are online communities where people express their issues, and seek help from moderators and other users. In such forums, there are often posts with severe content indicating that the user is in acute distress and there is a risk of attempted self‐harm. Moderators need to respond to these severe posts in a timely manner to prevent potential self‐harm. However, the large volume of daily posted content makes it difficult for the moderators to locate and respond to these critical posts. We propose an approach for triaging user content into four severity categories that are defined based on an indication of self‐harm ideation. Our models are based on a feature‐rich classification framework, which includes lexical, psycholinguistic, contextual, and topic modeling features. Our approaches improve over the state of the art in triaging the content severity in mental health forums by large margins (up to 17% improvement over the F‐1 scores). Furthermore, using our proposed model, we analyze the mental state of users and we show that overall, long‐term users of the forum demonstrate decreased severity of risk over time. Our analysis on the interaction of the moderators with the users further indicates that without an automatic way to identify critical content, it is indeed challenging for the moderators to provide timely response to the users in need.

Adverse Drug Reactions (ADRs) have become a serious health problem and even a leading cause of death in the United States. Pre‐marketing clinical trials and traditional post‐marketing surveillance using voluntary and spontaneous report systems are insufficient for ADR detection. On the other hand, online health forums provide valuable evidences in a large scale and in a timely fashion through the active participation of patients, caregivers, and doctors. In this article, we present patient‐centered and experience‐aware mining framework for effective ADR discovery using online health forum data. Our experimental evaluation with both an official ADR knowledge base and human‐annotated ground truth verifies the effectiveness of the proposed method for ADR discovery.

A non‐negligible fraction of user queries end up with very few or even no matching results in leading commercial web search engines. In this work, we provide a detailed characterization of such queries and show that search engines try to improve such queries by showing the results of related queries. Through a user study, we show that these query suggestions are usually perceived as relevant. Also, through a query log analysis, we show that the users are dissatisfied after submitting a query that match no results at least 88.5% of the time. As a first step towards solving these no‐answer queries, we devised a large number of features that can be used to identify such queries and built machine‐learning models. These models can be useful for scenarios such as the mobile‐ or meta‐search, where identifying a query that will retrieve no results at the client device (i.e., even before submitting it to the search engine) may yield gains in terms of the bandwidth usage, power consumption, and/or monetary costs. Experiments over query logs indicate that, despite the heavy skew in class sizes, our models achieve good prediction quality, with accuracy (in terms of area under the curve) up to 0.95.

We describe the intellectual and physical properties of books as manifestations, expressions, and works and assess the current indexing and metadata structure of monographs in the Book Citation Index (BKCI). Our focus is on the interrelationship of these properties in light of the Functional Requirements for Bibliographic Records (FRBR). Data pertaining to monographs were collected from the Danish PURE repository system as well as the BKCI (2005–2015) via their International Standard Book Numbers (ISBNs). Each ISBN was then matched to the same ISBN and family‐related ISBNs cataloged in two additional databases: OCLC‐WorldCat and Goodreads. With the retrieval of all family‐related ISBNs, we were able to determine the number of monograph expressions present in the BKCI and their collective relationship to one work. Our results show that the majority of missing expressions from the BKCI are emblematic (i.e., first editions of monographs) and that both the indexing and metadata structure of this commercial database could significantly improve with the introduction of distinct expression IDs (i.e., for every distinct edition) and unifying work‐related IDs. This improved metadata structure would support the collection of more accurate publication and citation counts for monographs and has implications for developing new indicators based on bibliographic levels.

New technology implementations impact organizational behavior and outcomes, sometimes in unintended ways. A combination of design decisions, altered affordances, and political struggles within a state cloud computing implementation reduced levels of service among records management professionals, in spite of their strongly expressed desire to manage records with excellence. Struggles to maintain ownership and control over organizational processes and resources illustrate the power dynamics that are affected by the design of a new system implementation. By designing the system with a single goal in mind (centralization to reduce costs), strategic management failed to consider otherwise predictable outcomes of reducing the resources controlled by a group with lesser power and increasing the resources controlled by an already dominant power within the institution. These findings provide valuable insights into the considerations which cloud computing designs should take into account. They also offer an understanding of changing educational requirements for records management workers to engage more effectively across occupations in technologically changing environments and the potential risks that cloud computing provide to productivity. The research was comprised of an extensive literature review, a grounded theory methodological approach, and rigorous data collection and synthesis via an empirical case study.

Offering one's perspective and justifying it has become a common practice in online text‐based communications, just as it is in typical, face‐to‐face communication. Compared to the face‐to‐face communications, it can be particularly more challenging for users to understand and evaluate another's perspective in online communications. On the other hand, the availability of the communication record in online communications offers a potential to leverage computational techniques to automatically detect user opinions and rationales. One promising approach to automatically detect the rationales is to detect the common discourse relations in rationale texts. However, no empirical work has been done with regard to which discourse relations are commonly present in the users’ rationales in online communications. To fill this gap, we annotated the discourse relations in the text segments that contain the rationales (N = 527 text segments). These text segments are obtained from five datasets that consist of five online posts and the first 100 comments. We identified 10 discourse relations that are commonly present in this sample. Our finding marks an important contribution to this rationale detection approach. We encourage more empirical work, preferably with a larger sample, to examine the generalizability of our findings.

In recent years, a number of studies have introduced methods for identifying papers with delayed recognition (so called “sleeping beauties,” SBs) or have presented single publications as cases of SBs. Most recently, Ke, Ferrara, Radicchi, and Flammini (, Proceedings of the National Academy of Sciences of the USA, 112(24), 7426–7431) proposed the so called “beauty coefficient” (denoted as B) to quantify how much a given paper can be considered as a paper with delayed recognition. In this study, the new term smart girl (SG) is suggested to differentiate instant credit or “flashes in the pan” from SBs. Although SG and SB are qualitatively defined, the dynamic citation angle β is introduced in this study as a simple way for identifying SGs and SBs quantitatively — complementing the beauty coefficient B. The citation angles for all articles from 1980 (n = 166,870) in natural sciences are calculated for identifying SGs and SBs and their extent. We reveal that about 3% of the articles are typical SGs and about 0.1% typical SBs. The potential advantages of the citation angle approach are explained.

Insider threat is a “wicked” contemporary organizational problem. It poses significant threats to organizational operations and information security. This article reviews insider threat research and outlines key propositions to conceptualize the interpretation of dynamic human information behavior in an organizational setting, which represent an integration of trustworthiness and human sensors’ attribution in close relationships. These propositions posit that when a focal individual violates integrity‐based trust, the group can collectively attribute a shift in trustworthiness, triggering a natural peer attribution process that assigns cause to observed behavior. Group communication can thus reflect subtle changes in a focal individual's perceived trustworthiness. The ability to understand group‐based computer‐mediated communication patterns over time may become essential in safeguarding information assets and the “digital well‐being” of today's organizations. This article contributes a novel theoretical lens to examine dynamic insights on insider threat detection.

Does patients’ Internet search of disease information affect their trust in physicians during diagnosis? This study proposes a research model from a knowledge power perspective, that is, Internet search affects patients’ perception of their knowledge level. Our empirical study of more than 400 subjects suggests that for patients who searched online for disease information, the inconsistency between their self‐diagnosis expectations and their physician's diagnosis reduces their trust in their physician. The effect is stronger for those who spent more time on Internet search. Patients with chronic conditions are less affected by the inconsistency, as are patients of physicians with a higher professional status. This study also found that physicians’ interaction quality in the diagnosis process—how well they communicate with their patient—still plays a dominant role in gaining patient's trust. This finding suggests that even in the high‐tech age, high‐touch remains an important factor to physician‐patient trust.

The challenge in executing a data science project is more than just identifying the best algorithm and tool set to use. Additional sociotechnical challenges include items such as how to define the project goals and how to ensure the project is effectively managed. This paper reports on a set of case studies where researchers were embedded within data science teams and where the researcher observations and analysis was focused on the attributes that can help describe data science projects and the challenges faced by the teams executing these projects, as opposed to the algorithms and technologies that were used to perform the analytics. Based on our case studies, we identified 14 characteristics that can help describe a data science project. We then used these characteristics to create a model that defines two key dimensions of the project. Finally, by clustering the projects within these two dimensions, we identified four types of data science projects, and based on the type of project, we identified some of the sociotechnical challenges that project teams should expect to encounter when executing data science projects.

This study provides evidence of data set mentions and citations in multiple disciplines based on a content analysis of 600 publications in PLoS One. We find that data set mentions and citations varied greatly among disciplines in terms of how data sets were collected, referenced, and curated. While a majority of articles provided free access to data, formal ways of data attribution such as DOIs and data citations were used in a limited number of articles. In addition, data reuse took place in less than 30% of the publications that used data, suggesting that researchers are still inclined to create and use their own data sets, rather than reusing previously curated data. This paper provides a comprehensive understanding of how data sets are used in science and helps institutions and publishers make useful data policies.

Social influence has a great impact on human behavior, which has been widely investigated in various research fields. Even so, it has rarely been investigated in the online health community. In this paper, we focus on the multichannel access in online health communities, defining social influence as the average degree of multichannel access to a physician's colleagues. Based on the multinomial logistic regression model, we examined the direct effects of social influence and patients' rating to multichannel access. In addition, we explored the moderating effect of social influence on the relationship between patients' rating and multichannel access in online health communities. The results of the experiment and robustness testing support the propositions that social influence and patients' rating significantly and positively affect multichannel access in an online health community. The moderating effect of social influence is negative and significantly influences the accessible channels provided by the focal physician. This research contributes to the literature concerning online health communities, social influence, and multichannel access; it also has practical implications.

This review of the literature presents an overview of the last 50 years of research on acknowledgments in the context of scholarly communication. Through qualitative coding and bibliometric methods, this meta‐synthesis provides an in‐depth description of acknowledgments research and reveals the five main thematic categories that emerge from this corpus of literature. Adopting a historical approach, this review shows a diversified and scattered research landscape. Despite five decades of analysis putting forward the potential value of acknowledgments as markers of scientific capital, the literature still lacks consensus as to the value and functions of acknowledgments within the reward system of science.

Scientific collaboration is vital to many fields, and it is common to see scholars seek out experienced researchers or experts in a domain with whom they can share knowledge, experience, and resources. To explore the diversity of research collaborations, this article performs a temporal analysis on the scientific careers of researchers in the field of computer science. Specifically, we analyze collaborators using 2 indicators: the research topic diversity, measured by the Author‐Conference‐Topic model and cosine, and the impact diversity, measured by the normalized standard deviation of h‐indices. We find that the collaborators of high‐impact researchers tend to study diverse research topics and have diverse h‐indices. Moreover, by setting PhD graduation as an important milestone in researchers' careers, we examine several indicators related to scientific collaboration and their effects on a career. The results show that collaborating with authoritative authors plays an important role prior to a researcher's PhD graduation, but working with non‐authoritative authors carries more weight after PhD graduation.

The information sciences have traditionally been at the center of metadata‐focused research. The US National Security Agency (NSA) intelligence documents revealed by Edward Snowden in June of 2013 brought the term “metadata” into the public consciousness. Surprisingly little discussion in the information sciences has since occurred on the nature and importance of metadata within networked communication systems. The collection of digital metadata impacts the ways that people experience social and technical communication. Without such metadata, networked communication cannot exist. The NSA leaks, and numerous recent hacks of corporate and government communications, point to metadata as objects of new scholarly inquiry. If we are to engage in meaningful discussions about our digital traces, or make informed decisions about new policies and technologies, it is essential to develop theoretical and empirical frameworks that account for digital metadata. This opinion paper presents 5 key sociotechnical characteristics of metadata within digital networks that would benefit from stronger engagement by the information sciences.

Scientific collaboration is essential in solving problems and breeding innovation. Coauthor network analysis has been utilized to study scholars' collaborations for a long time, but these studies have not simultaneously taken different collaboration features into consideration. In this paper, we present a systematic approach to analyze the differences in possibilities that two authors will cooperate as seen from the effects of homophily, transitivity, and preferential attachment. Exponential random graph models (ERGMs) are applied in this research. We find that different types of publications one author has written play diverse roles in his/her collaborations. An author's tendency to form new collaborations with her/his coauthors' collaborators is strong, where the more coauthors one author had before, the more new collaborators he/she will attract. We demonstrate that considering the authors' attributes and homophily effects as well as the transitivity and preferential attachment effects of the coauthorship network in which they are embedded helps us gain a comprehensive understanding of scientific collaboration.

One way to facilitate Multilingual Information Access (MLIA) for digital libraries is to generate multilingual metadata records by applying Machine Translation (MT) techniques. Current online MT services are available and affordable, but are not always effective for creating multilingual metadata records. In this study, we implemented 3 different MT strategies and evaluated their performance when translating English metadata records to Chinese and Spanish. These strategies included combining MT results from 3 online MT systems (Google, Bing, and Yahoo!) with and without additional linguistic resources, such as manually‐generated parallel corpora, and metadata records in the two target languages obtained from international partners. The open‐source statistical MT platform Moses was applied to design and implement the three translation strategies. Human evaluation of the MT results using adequacy and fluency demonstrated that two of the strategies produced higher quality translations than individual online MT systems for both languages. Especially, adding small, manually‐generated parallel corpora of metadata records significantly improved translation performance. Our study suggested an effective and efficient MT approach for providing multilingual services for digital collections.

This study advances a theoretical model centered on collective and internal efficacy to explain the separate pathways through which political sharing on Facebook and Twitter may influence individuals to engage in political activities. We test the model with data from a 2‐wave panel survey conducted with an adult population in 2013 in Chile. We found that frequent usage of Facebook and Twitter for sharing political information is conducive to higher levels of participation through different efficacy measures. Facebook has a significant effect on collective—not internal—efficacy, whereas Twitter's effect is on internal—not collective—efficacy. Results are discussed in light of the diverse affordances and strengths of network ties of Facebook and Twitter.

Many algorithms for searching the main paths according to a subfield technology have been suggested using patent citation networks. However, the process by which the specific field is divided into subfields is problematic, in the sense that citation information among individual patents in different subfields is lost. If convergence technologies are common among different subfields, this problem can result in an inappropriate main path being found. To resolve this problem, we propose a new algorithm capable of extracting the main paths for each subfield of a technology by using the International Patent Classification (IPC) codes of patents. The proposed algorithm is applied to the waste management field to allow core technologies in the subfields of waste disposal, waste treatment, and the reuse of waste materials to be found. Finally, a sensitivity analysis is performed by considering the standard of the component ratio along a specific path to demonstrate the robustness of the proposed algorithm in the waste management field.

In this paper, we perform an in‐depth study into how clinicians represent their information needs and the influence this has on information retrieval (IR) effectiveness. While much research in IR has considered the effectiveness of IR systems, there is still a significant gap in the understanding of how users contribute to the effectiveness of these systems. The paper aims to contribute to this by studying how clinicians search for information. Multiple representations of an information need—from verbose patient case descriptions to ad‐hoc queries—were considered in order to understand their effect on retrieval. Four clinicians provided queries and performed relevance assessment to form a test collection used in this study. The different query formulation strategies of each clinician, and their effectiveness, were investigated. The results show that query formulation had more impact on retrieval effectiveness than the particular retrieval systems used. The most effective queries were short, ad‐hoc keyword queries. Different clinicians were observed to consistently adopt specific query formulation strategies. The most effective queriers were those who, given their information need, inferred novel keywords most likely to appear in relevant documents. This study reveals aspects of how people search within the clinical domain. This can help inform the development of new models and methods that specifically focus on the query formulation process to improve retrieval effectiveness.

This article demonstrates the benefits of using sequence representations based on word embeddings to inform the seed selection and sample selection processes in an active learning pipeline for clinical information extraction. Seed selection refers to choosing an initial sample set to label to form an initial learning model. Sample selection refers to selecting informative samples to update the model at each iteration of the active learning process. Compared to supervised machine learning approaches, active learning offers the opportunity to build statistical classifiers with a reduced amount of training samples that require manual annotation. Reducing the manual annotation effort can support automating the clinical information extraction process. This is particularly beneficial in the clinical domain, where manual annotation is a time‐consuming and costly task, as it requires extensive labor from clinical experts. Our empirical findings demonstrate that (a) using sequence representations along with the length of sequence for seed selection shows potential towards more effective initial models, and (b) using sequence representations for sample selection leads to significantly lower manual annotation efforts, with up to 3% and 6% fewer tokens and concepts requiring annotation, respectively, compared to state‐of‐the‐art query strategies.