Search results

Knowledge sharing in online health communities (OHCs) disrupts consumers' health information-seeking behavior patterns such as seeking health information and consulting. Based on social exchange theory, this study explores how the two dimensions of experts' free knowledge sharing (general and specific) affect customer transactional and nontransactional engagement behavior and how the quality of experts' free knowledge sharing moderates the above relationships.

We adopted negative binomial regression models using homepage data of 2,982 experts crawled from Haodf.com using Python.

The results show that experts' free general knowledge sharing and free specific knowledge sharing positively facilitate both transactional and nontransactional engagement of consumers. The results also demonstrate that experts' efforts in knowledge-sharing quality weaken the positive effect of their knowledge-sharing quantity on customer engagement.

This study provides new insights into the importance of experts' free knowledge sharing in OHCs. This study also revealed a “trade-off” between experts' knowledge-sharing quality and quantity. These findings could help OHCs managers optimize knowledge-sharing recommendation mechanisms to encourage experts to share more health knowledge voluntarily and improve the efficiency of healthcare information dissemination to promote customer engagement.

Drawing on Suchman’s conception of cognitive legitimacy and Boswell’s account of the political functions of expert knowledge, this paper aims to study the due process followed by the International Integrated Reporting Council (IIRC) prior to the publication of the first version of the International Integrated Reporting Framework (IIRF). Specifically, the author analyses the lobbying strategies used in the comment letters sent by a subset of lobbyists, “the experts”, represented by accounting bodies and firms, regulators and academics.

From both a form- and meaning-oriented analysis, this paper focuses on how the experts resorted to the functions of knowledge when they took part in the IIRF’s public consultation. The author first carries out a quantitative content analysis of the responses to the 2013 Consultation Draft submitted by those constituents considered as accounting expert lobbyists. Then, the author analyse how these actors framed their comments under expert knowledge to legitimise the IIRC, the IIRF and the accounting profession itself.

The findings suggest that the expert groups welcomed the opportunity, not simply to legitimise the IIRC through their democratic support, but to provide a technocratic settlement that ensures the due process is based on the mobilisation of expert knowledge as a legitimate source. By drawing on the cognitive legitimacy of expert lobbyists, the IIRC drew on the political functions of expert knowledge to reduce uncertainty and gain stability.

Analysis of the lobbying strategies used by the accounting experts whose position could make a difference and receive more attention from the IIRC makes this contribution of particular interest, especially since the first version of the IIRF sought to guide disclosure and sustainable business practices around the world.

Experts as political actors play a legitimising role since they are capable of producing relevant knowledge that, due to its nature and scope, certainly affects policymaking and sustainable development.

This research provides a sociopolitical perspective to comprehend how some lobbying strategies, in this case, of expert actors, contribute to legitimising a standard-setter body and its endeavours in the context of voluntary standards.

Credit risk evaluation is a crucial task for banks and non-bank financial institutions to support decision-making on granting loans. Most of the current credit risk methods rely solely on expert knowledge or large amounts of data, which causes some problems like variable interactions hard to be identified, models lack interpretability, etc. To address these issues, the authors propose a new approach.

First, the authors improve interpretive structural model (ISM) to better capture and utilize expert knowledge, then combine expert knowledge with big data and the proposed fuzzy interpretive structural model (FISM) and K2 are used for expert knowledge acquisition and big data learning, respectively. The Bayesian network (BN) obtained is used for forward inference and backward inference. Data from Lending Club demonstrates the effectiveness of the proposed model.

Compared with the mainstream risk evaluation methods, the authors’ approach not only has higher accuracy and better presents the interaction between risk variables but also provide decision-makers with the best possible interventions in advance to avoid defaults in the financial field. The credit risk assessment framework based on the proposed method can serve as an effective tool for relevant policymakers.

The authors propose a novel credit risk evaluation approach, namely FISM-K2. It is a decision support method that can improve the ability of decision makers to predict risks and intervene in advance. As an attempt to combine expert knowledge and big data, the authors’ work enriches the research on financial risk.

Indigenous knowledge is an essential element for unveiling the evolutionary journey of socio-culture phenomena. One of the key challenges in foresight exercises is to incorporate social-culture issues such as culture, lifestyle and behavior (referred as indigenous knowledge) into the study. However, the statistical trends of those factors tend to be either not available or limited unlike the population or economic related factors. The purpose of this study is to present the use of valuable data from indigenous knowledge to enhance the foresight exercise through the better understanding of social dynamics and changes.

The fragmented form of indigenous knowledge is analyzed and converted into a structured data format and then interpreted to unveil the evolutionary journey of socio-cultural phenomena. This study applies a scenario development method to visualize the results of foresight by comparing before and after the integration of indigenous knowledge. Finally, an assessment was conducted to reflect the value enhancement resulting from the integration of indigenous knowledge into the foresight process.

With the proposed approach, the foresight study on the future development of Thai food was demonstrated. The findings of this study show that the use of indigenous knowledge on eating behavior, cooking style and food flavor helps improve the alternative scenarios for the future development of Thai foods.

Indigenous knowledge can be applied to develop plausible scenarios and future images in foresight exercises. However, by nature, indigenous knowledge is not well-structured and, therefore, needs to be analyzed and turned into structured data so that it can be interpreted before integrating into the foresight process.

This study is one of few studies addressing the opportunities for integrating indigenous knowledge into foresight process. Indigenous knowledge can unveil the evolution of socio-cultural changes to improve the results of foresight study, especially the cases where statistical data and trends may not be sufficient to foresee future development.

In traditional Bayesian belief networks (BBNs), a large amount of data are required to complete network parameters, which makes it impractical. In addition, no systematic method has been used to create the structure of the BBN. The aims of this study are to: (1) decrease the number of questions and time and effort required for completing the parameters of the BBN and (2) present a simple and apprehensible method for creating the BBN structure based on the expert knowledge.

In this study, by combining the decision-making trial and evaluation laboratory (DEMATEL), interpretive structural modeling (ISM) and BBN, a model is introduced that can form the project risk network and analyze the impact of risk factors on project cost quantitatively based on the expert knowledge. The ranked node method (RNM) is then used to complete the parametric part of the BBN using the same data obtained from the experts to analyze DEMATEL.

Compared to the traditional BBN, the proposed method will significantly reduce the time and effort required to elicit network parameters and makes it easy to create a BBN structure. The results obtained from the implementation of the model on a mass housing project showed that considering the identified risk factors, the cost overruns relating to material, equipment, workforce and overhead cost were 37.6, 39.5, 42 and 40.1%, respectively.

Compared to the traditional BBN, the proposed method will significantly reduce the time and effort required to elicit network parameters and makes it easy to create a BBN structure. The results obtained from the implementation of the model on a mass housing project showed that considering the identified risk factors, the cost overruns relating to material, equipment, workforce and overhead cost were 37.6, 39.5, 42 and 40.1%, respectively. The obtained results are based on a single case study project and may not be readily generalizable.

The presented framework makes the BBN more practical for quantitatively assessing the impact of risk on project costs. This helps to manage financial issues, which is one of the main reasons for project bankruptcy.

Accurate prediction of the structural condition of urban critical infrastructure is crucial for predictive maintenance. However, the existing prediction methods lack precision due to limitations in utilizing heterogeneous sensing data and domain knowledge as well as insufficient generalizability resulting from limited data samples. This paper integrates implicit and qualitative expert knowledge into quantifiable values in tunnel condition assessment and proposes a tunnel structure prediction algorithm that augments a state-of-the-art attention-based long short-term memory (LSTM) model with expert rating knowledge to achieve robust prediction results to reasonably allocate maintenance resources.

Through formalizing domain experts' knowledge into quantitative tunnel condition index (TCI) with analytic hierarchy process (AHP), a fusion approach using sequence smoothing and sliding time window techniques is applied to the TCI and time-series sensing data. By incorporating both sensing data and expert ratings, an attention-based LSTM model is developed to improve prediction accuracy and reduce the uncertainty of structural influencing factors.

The empirical experiment in Dalian Road Tunnel in Shanghai, China showcases the effectiveness of the proposed method, which can comprehensively evaluate the tunnel structure condition and significantly improve prediction performance.

This study proposes a novel structure condition prediction algorithm that augments a state-of-the-art attention-based LSTM model with expert rating knowledge for robust prediction of structure condition of complex projects.

Blockchain is a disruptive technology that has matured to deliver robust, global, IT systems, yet adoption lags predictions. The authors explore barriers to adoption in the context of a global challenge with multiple stakeholders: integration of carbon markets. Going beyond the dominant economic-rationalistic paradigm of information system (IS) innovation adoption, the authors reduce pro-innovation bias and broaden inter-organizational scope by using technological frames theory to capture the cognitive framing of the challenges perceived within the world’s largest carbon emitter: China.

Semi-structured interviews with 15 key experts representing three communities in China’s carbon markets: IT experts in carbon markets; carbon market experts with conceptual knowledge of blockchain and carbon market experts with practical blockchain experience.

Perceived technical challenges were found to be the least significant in explaining adoption. Significant challenges in five areas: social, political legal and policy (PLP), data, organizational and managerial (OM) and economic, with PLP and OM given most weight. Mapping to frames developed to encompass these challenges: nature of technology, strategic use of technology and technology readiness resolved frame incongruence that, in the case explored, did not lead to rejection of blockchain, but a decision to defer investment, increase the scope of analysis and delay the adoption decision.

Increases scope and resolution of IS adoption research. Technological frames theory moves from predominant economic-rational models to a social cognitive perspective. Broadens understanding of blockchain adoption in a context combining the world’s most carbon emissions with ownership of most blockchain patents, detailing socio-technical challenges and delivering practical guidance for policymakers and practitioners.

The internationalisation of industries has spilled over to academia, whereby institutions of higher learning (IHL) increasingly compete in the graduate quality and applied graduate knowledge capabilities that they can offer. With increasing global competition for students, combined with the evolving need for lifelong learning in dynamic industries impacted by digital knowledge management, there is an opportunity for IHLs to be able to evolve to ensure their business models enable services and service delivery to cater to and help shape industry demands. This chapter will look at micro-credentialing (MC) and how the provision of MCs has changed along with the evolving IHL education environment. The demands of students, employers and ecosystem considerations will be addressed through a review of the current landscape, pathways to MC and how MC may be operationalised. The Bersteinian approach to pedagogic classification, which identifies the framework of knowledge as being communicable via three axes of singularism, regionalism and a wider generalist approach is referenced as a framework. The resultant recommendations that draw upon these foundations will conclude the chapter.

There is a growing need for methodological plurality in advancing operations management (OM), especially with the emergence of machine learning (ML) techniques for analysing extensive textual data. To bridge this knowledge gap, this paper introduces a new methodology that combines ML techniques with traditional qualitative approaches, aiming to reconstruct knowledge from existing publications.

In this pragmatist-rooted abductive method where human-machine interactions analyse big data, the authors employ topic modelling (TM), an ML technique, to enable constructivist grounded theory (CGT). A four-step coding process (Raw coding, expert coding, focused coding and theory building) is deployed to strive for procedural and interpretive rigour. To demonstrate the approach, the authors collected data from an open-source professional project management (PM) website and illustrated their research design and data analysis leading to theory development.

The results show that TM significantly improves the ability of researchers to systematically investigate and interpret codes generated from large textual data, thus contributing to theory building.

This paper presents a novel approach that integrates an ML-based technique with human hermeneutic methods for empirical studies in OM. Using grounded theory, this method reconstructs latent knowledge from massive textual data and uncovers management phenomena hidden from published data, offering a new way for academics to develop potential theories for business and management studies.