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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.

This study aims to investigate how sensory cue order, wine knowledge and visual–olfactory (V–O) congruence affect consumer’s taste perceptions of wine and their subsequent behavior.

An experiment was performed to identify the effects of sensory cue congruence and sensory cue order on wine consumers’ perceptions of wine, affective evaluations, cognitive evaluations and purchase intentions.

Wine experts exhibited positive emotional responses to congruent sensory cues in the V–O order. Experts’ enjoyment of wine’s aroma, their emotional responses, their cognitive evaluations and their purchase intentions were lower in the incongruent condition. Consumers’ negative emotions elicited by the V–O sequence were also less intense than those triggered by the olfactory–visual (O–V) sequence. Wine experts demonstrated more positive emotional responses in the V–O sensory congruent condition.

This study highlights how visual and olfactory sensory cue order, wine knowledge and sensory cue congruence interact to clarify wine-related behavioral intention. Findings reveal the roles of these factors in shaping sensory perceptions, cognitive evaluations, affective evaluations and behavior related to wine consumption.

This study holds implications for various stakeholders, including winemakers, wine businesses, restaurants and the broader hospitality industry. Wine businesses can enhance advertising effectiveness by tailoring their marketing efforts to customers’ knowledge levels and emphasizing the inherent attributes that align with individuals’ preferences. Winemakers can improve consumers’ sensory experiences by enhancing the natural color of wines. Restaurants can strive to ignite diners’ positive emotions and experiences by providing congruent information. Furthermore, sensory-driven strategies can be used in the hospitality sector to elevate customers’ positive emotions.

This study fills gaps in wine research by delineating how wine knowledge and related sensory cues can influence consumers’ sensory perceptions, cognitive evaluations, affective evaluations and behavior. These aspects have been largely overlooked in previous work.

Engineering knowledge continuity is crucial for the life cycle management of long-lived and complex assets, such as nuclear plants, locks and storm surge barriers. At the storm surge barriers in the Netherlands, engineering knowledge continuity is not yet fully assured, despite long-standing efforts. This study aims to explore the relationship between system characteristics, the organizational demarcation of maintenance and operation and the challenges in achieving engineering knowledge continuity and provides suggestions for improvement of theory and policy.

Ten semi-structured interviews were conducted with professionals from various backgrounds in construction, engineering and asset management of the Dutch storm surge barriers, augmented with visits to barriers and barrier teams. A thematic analysis was used to identify and describe the challenges to engineering continuity, their origins and potential solutions. We reviewed knowledge management policy documents and asset management consultancy reports to validate the findings. Additionally, we engaged in frequent interactions with professionals at the barriers. We achieved saturation and validation once no new issues were raised during these discussions.

The thematic analysis developed multiple themes describing the challenges to engineering continuity, their origins and potential solutions. The key findings are that expert engineers are critically important to deal with redesigns induced by obsolescence. Moreover, due to barrier uniqueness, long redesign cycles and reliability requirements, conventional knowledge continuity tools are insufficient to enable new engineers to reach expert level. Finally, the thematic analysis shows that, in some cases, outsourcing should be reduced to facilitate internal learning.

The study introduces the application of the knowledge-based view of the firm and the concept of requisite knowledge redundancy to the long-term management of complex assets. It calls for more attention to long gaps in the use of unique knowledge and the effect on knowledge continuity.

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.

Megaproject knowledge innovation (MKI) is perceived as a critical strategy for engineering value co-creation and industrial chain upgrading. Ascertaining the impact mechanism of MKI is a crucial initial step towards improving management practices. Within the framework of complex systems in megaprojects, factors exhibit intricate interdependencies. However, the current domain of knowledge has either overlooked or oversimplified this relationship and therefore cannot propose pragmatic and efficacious strategies for enhancing MKI. To close this gap, this study develops a Bayesian network (BN) model aiming to investigate the interdependencies among MKI-related factors and their impact on MKI.

First, this study implements literature review, expert interview and field investigation to identify the influencing factor nodes for the network model development. Second, a Bayesian network was constructed by integrating the expert knowledge with Dempster-Shafer theory. Next, a MKI measurement model was established using 253 training samples. Finally, the factor significance and optimal MKI improvement strategies are identified from the sensitivity analysis and probabilistic reasoning within the BNs.

The results indicate that (1) the BN model exhibits significant reliability and holds promotion and application value in formulating MKI management strategies; (2) knowledge sharing, shared vision and leadership are the key influencing factors of MKI; and (3) simultaneously improving institutional pressure, leadership and knowledge sharing is the most optimal strategy to enhance MKI.

This study innovatively introduced the BN method into the domain of MKI management, providing an appropriate approach for modelling complex relationships among factors and investigate nonlinear influences. The developed model raises megaproject stakeholders’ awareness about factors influencing MKI and presents quantified strategies that increase the likelihood of maximising MKI levels. Its ease of generalisability positions it as a promising decision support tool, facilitating the implementation of sustainable MKI practices.

Small and medium-sized enterprises (SMEs) are a major source of employment and revenue growth in developing nations like India, but they also face challenges from resource shortages, shifting consumer demand and heightened competition. This research aims to discover the aspects that enhance SMEs' competitiveness and performance.

By analyzing literature and consulting experts, 10 factors that boost a firm's competitiveness were identified. The total interpretive structural modeling (TISM) method was then used to determine their interaction and structural hierarchy. Neutrosophic-MICMAC analysis was employed to assess the driving-dependence power of each factor.

The study discovered that the factor, namely “entrepreneurial orientation,” was found to be a significant one. “Manufacturing strategy” was found to be extremely dependent on the remaining competitive advantage factors.

This SME-focused framework can be adopted by large businesses to enhance organizational performance by focusing on critical factors. The study depends on experts' judgment, which might be biased. Findings will assist SMEs in identifying significant factors influencing competitive advantage and relationships, increasing awareness of factors contributing to competitive edge.

The results of the research may encourage SME sector managers and practitioners to prioritize the factors that contribute to a firm's competitive advantage.

The majority of research on SME competitive advantage focuses on individual aspects. To add to the body of knowledge on the subject, this study applies the TISM technique to Indian SMEs to identify the contextual interactions among factors that increase long-term competitiveness.

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.

In response to water scarcity in Sri Lanka, the government is implementing strategies such as rainwater harvesting, efficient irrigation, wastewater treatment and desalination. Initial efforts include the establishment of a desalination plant in Jaffna, with additional plans for the dry zones (DZ). The study aims to comprehensively identify the barriers to establishing desalination plants in the DZ and provide recommendations to mitigate these barriers. Additionally, this research provides valuable insights aimed at minimizing barriers to the construction of future desalination plants within Sri Lanka.

The study used qualitative methods, using an expert survey to identify current and future barriers, along with strategies for overcoming them. The collected data were analysed using the template analysis technique.

Regarding desalination plant establishment, various barriers such as high capital costs, high energy expenses, brine discharge, pollution, emissions, technical challenges, health concerns and waste disposal have been identified. However, specific strategies exist to address and mitigate each of these obstacles.

The study offers recommendations to environmental experts and government on expediting the approval procedures for desalination plants in Sri Lanka’s DZ. Adapted to Sri Lanka’s specific challenges, it highlights strategies and barriers essential for upcoming desalination projects. Furthermore, it emphasizes the financial advantages such as increased production and job creation resulting from establishing desalination facilities.

Through this study, promoting sustainable practices and fostering community involvement, it aims to enhance livelihoods, accelerate economic development and improve overall well-being through reliable access to water. Additionally, the study aims to enhance understanding of the importance of desalination in alleviating water scarcity, promoting community engagement and ultimately facilitating improved living conditions, health outcomes and economic opportunities in Sri Lanka’s DZs.

This study provides crucial direction for decision-makers by highlighting the main barriers to the establishment of desalination plants in Sri Lanka and outlining practical solutions. Implementing these strategies helps meet the region’s increasing water demands, advance sustainable water management, improve the standard of living for nearby communities and promote the socioeconomic development of desalination plants in Sri Lanka’s DZ.

It is crucial to transform current enterprises to greener versions of them to reach the sustainable development goals. The first step of this transformation can be understanding comprehensively environmental performances of enterprises. This study presents a practical analysis for evaluation of factors affecting environmental performance of enterprises to call them as a “dark green.”

For this purpose, a detailed factor search was primarily performed and then the weights of them on environmental performance of the enterprises to support sustainable development were analyzed using fuzzy cognitive map (FCM) that incorporates the casual relationships between factors and represents the dynamics of the complex systems. The FCM was also supported with extended great deluge algorithm (EGDA), which is an evolutionary algorithm with high performance to increase robustness of the study.

The findings indicated that the most influential factors on environmental performance of an activist enterprise are “loyalty to regulations,” “digitalization level,” “tendency to produce environmentally friendly products/services,” “productivity efforts” and “fossil fuel consumption,” respectively. While the first four of them affect the environmental performance positively, fossil fuel consumption affects it negatively.

The results of this study can help companies to prioritize the critical points for their environmental perspectives, observe at which factors they are good or lacking and find where to start improvement.

This study is one of the pioneering studies to investigate the importance of criteria for a dark green business, considering 21 factors from different sources to make a detailed representation of corporate environmental sustainability.

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.