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The purpose of this study is to provide ideas and theoretical guidance for green, environmentally friendly and efficient “bacteriostasis with bacteria” technology.

In this paper, a beneficial strain of bacteria was extracted and purified from marine mud. Weight-loss test, morphological observation and electrochemical test were used to systematically study the effect of sulfate-reducing bacteria (SRB)-induced corrosion inhibition on X65 steel in simulated offshore oil field production water.

The results showed that a beneficial strain was selected and identified as Vibrio alginolyticus. Under the condition of co-culture of SRB, the average corrosion rate of X65 steel was significantly reduced. In the mixed bacterial system, the surface of X65 steel samples was relatively flat, and the structure of biofilm and corrosion product film was dense. The number of corrosion pits, the average diameter and depth of corrosion pits were significantly reduced. The localized corrosion of X65 steel was significantly inhibited.

The complex and changing marine environment makes the corrosion problem of marine steel increasingly severe, and the microbiologically influenced corrosion (MIC) caused by SRB is particularly serious. The research and development of environmentally friendly corrosion protection technology is a long-term and difficult problem. The use of beneficial microorganisms to control MIC is a green and efficient anticorrosion measure. Compared with terrestrial microorganisms, marine microorganisms can adapt to complex environments, and their metabolites exhibit special biological activities. The use of marine beneficial bacteria can inhibit SRB activity to achieve the corrosion inhibition effect.

Quality function deployment (QFD) has been widely applied in new product development, but existing research on QFD has some limitations. Primarily, QFD lacks the capability to provide feedback on the satisfaction degree of customer requirements (CRs) according to the actual values of engineering characteristics (ECs). In addition, QFD does not quantitatively consider the interrelationships among ECs. Reverse QFD (R-QFD) was introduced to implement the feedback process. On this basis, this paper quantitatively considers the interrelationships among ECs in the R-QFD model and extends these relationships to encompass combinations of multiple ECs, aiming to improve the inference accuracy of the model.

A nonlinear regression model was established between CRs and ECs, aiming to infer the satisfaction degree of CRs based on the implementation status of ECs. This model considers the interdependencies among ECs and extends the consideration of pairwise EC correlations from every two to every fifteen. Lingo Software is utilized to seek solutions for this program. To facilitate the implementation of the program, a directive to simplify the solution has been proposed.

The experimental results indicate that the interrelationships among ECs significantly affect the inference accuracy of the R-QFD model, thereby verifying the necessity of considering higher-order interrelationships among ECs within the R-QFD framework. Based on the results from data experiments, this paper also proposes research recommendations pertaining to ECs hierarchy for varying quantities of ECs.

The outcomes of this study have further refined the R-QFD model, addressing its limitations of ignoring the interrelationships among ECs. This transformation elevates the R-QFD model from a relatively simple linear model to a nonlinear model formed through modeling, thereby enhancing its accuracy and applicability. In practical terms, this study provides case support for the application of the R-QFD model in manufacturing industry.

The study examines how process/organizational innovation and R&D spending mediate the relationship between financial performance and the resource dependence theory in Fintech, providing insights into effective innovation strategies for achieving sustainable financial performance.

Data from 191 financial firms in Taiwan was collected from annual reports using the Taiwan Economic Journal (TEJ), a financial information provider. Content analysis was used to measure innovation activities and financial performance, with process and organizational innovation defined. R&D expenditures were also collected and used in statistical analysis to explore the relationship between variables.

This study on the financial services industry shows that process innovation and R&D expenditure positively impact firm performance, while organizational innovation may have a negative short-term effect but could have long-term benefits.

Limitations of this study include vulnerability to spurious effects and the use of data from only listed financial service firms. Future research should use more short-term performance data and include unlisted firms in the financial services industry to extend the study’s coverage.

This study extends resource dependence theory to financial services and explores the effects of process and organizational innovation on firm performance. Results show that internal process management boosts performance, while external collaboration with startups enhances Fintech innovation and efficiency, with positive short-term effects. The study highlights the importance of interacting with external organizations to access resources and improve performance in financial services.

In public health emergencies, seeking confirmed cases’ activity trajectory information (CCATI) is crucial to the public’s efforts to combat the epidemic. The public can stabilize their sentiments and mitigate the risk of cross-infection by obtaining CCATI. We investigated the factors influencing users' intentions to seek CCATI to enhance the government’s risk communication capabilities and improve information platform services.

We analyzed how information ecological factors affect the intention to seek CCATI through perceived value. Data was collected from 429 Chinese citizens during the fourth wave of the coronavirus disease 2019 (COVID-19) pandemic. We used the structural equation model technology and bootstrap mediation effect test to examine the model.

Information understandability, information relevance, perceived severity and perceived vulnerability directly and positively affect the intention of seeking CCATI. While, the above relationships are also partially mediated by emotional value and functional value. Social support directly and negatively affects the intention of seeking CCATI, while the relationship is also partially mediated by emotional value and functional value. Curiosity directly and positively affects the intention of seeking CCATI, while the relationship is also partially mediated by emotional value. The relationship between the quality of the search service and the intention of seeking CCATI is not significant, instead, it is fully mediated by functional value. The influence effect of information relevance on the intention of seeking CCATI is the greatest, followed by perceived vulnerability. The mediating effect of functional value is higher than emotional value.

The findings may help governments enhance their risk communication capabilities and improve epidemic prevention and control measures, enhancing the appeal of information platforms.

We focused on CCATI, an area with limited scholarly attention. We analyzed CCATI-seeking factors using an information ecology theory, introducing perceived value as a mediator, thus offering novel perspectives and models.

Online health communities (OHCs) have been recognized as emerging platforms on the Internet used for health purposes. Despite its emergence, developing a successful OHC is still a challenge. Prior studies identified that value co-creation behavior (VCB) of members is an essential factor for sustaining OHCs; however, little is known about how members’ behavior drives to co-create value? Therefore, this study aims to discover the inclusive mechanism for members’ VCB in OHCs.

The authors develop the study model and hypothesis based on the service-dominant logic of value co-creation theory and social support (SS) literature. The survey data of 608 active OHCs users in China were analyzed using partial least squares structural equation modeling (PLS-SEM).

The results revealed that SS positively affects members’ VCBs. Ethical aspects; Trust and ethical interaction (EI) partially mediate their relationships. In addition, community members’ current health status (CHS) negatively moderates the relationships between SS and VCB. From the findings, it becomes evident that only SS is not enough; developing an ethical environment in OHCs, i.e. trust and ethically rich interactions among members, significantly helps OHCs to promote co-creation. Also, the negative moderation of CHS findings provides novel insights when cramming health conditions.

Exploring the complex mechanism of co-creation in OHC, the authors illustrate the potential of service-dominant logic to create new theoretical insight for healthcare and provide the framework of co-creation with ethics for the first time. This will extend the application of ethics in healthcare services and offer a robust platform from which the understanding of drivers of members’ VCB can be advanced in the OHC context.

In this study, suggestions on the protection and development of marine resources, sea area use and management, improvement of laws and regulations, monitoring of marine resources and impact assessment of marine environment are put forward.

Literature research method.

A major factor contributing to the decline in fishery resources is excessive fishing. At present, there are many problems to be solved in the exploitation and utilization of marine mineral resources in China. The pollution problem of marine tourism resources is becoming increasingly serious. Overmining of coastal sea resources has led to planning and management failures.

This paper discusses the current situation of marine resource exploitation and protection in China and analyzes the reasons for excessive resource exploitation from three aspects of marine concept, laws and regulations and marine management.

Online health communities (OHCs) have emerged on the Internet, substantially changing the conventional healthcare delivery model. Despite this emergence, the lack of patient participation and contribution always limits the success and sustainability of OHCs. Previous studies have disclosed that patients’ value co-creation behavior (VCB) helps organizations sustain OHCs. However, how the recent surge in artificial intelligence (AI) tools, such as social support chatbots (SSCs), drives patients’ VCB is still unknown. Therefore, this study examines the complex mechanism behind patients’ VCB to establish sustainable OHCs.

Using value co-creation and social support theories, the author develops a moderated mediation model and analyzes survey data from 338 respondents using partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN) methods.

Results demonstrate that perceived social support (PSS) from SSCs positively affects VCB directly and indirectly via patient learning (PL). This indirect effect is stronger when patient ability/readiness (PAR) is high. ANN findings highlight the model’s robustness and the significant role of PAR in VCB.

This study’s integrated framework offers unique insights into key drivers of patients’ VCB in OHCs. The findings indicate that PSS from SSCs enhances PL and VCB, with PAR influencing the strength of these relationships. Understanding these dynamics can inform user-centric interventions to promote effective learning and collaboration in OHCs.

Given the progressive rise of online health communities (OHC) that have predominantly changed health delivery services, healthcare organizations still face tremendous challenges of low patient participation and lack of high-quality contribution to OHC. Prior scholars indicated that inducing patient value co-creation behavior (VCB) is substantially beneficial for the sustainable growth of OHCs. However, what drives patients' behavior to co-create value is still unknown. To fill this important gap, this study used the service-dominant logic of value co-creation theory and face (mianzi in Chinese) literature to discover how patient co-creation attitude (CA) affects patient VCB. Also, this study aimed to explore the joint mechanism of how face gain (FG) and face loss (FL) impact patients' VCB in OHCs.

The survey data of 322 patients actively using OHC in China were analyzed via partial least squares structural equation model (PLS-SEM) and fuzzy set qualitative comparative analysis (fsQCA).

The results revealed that patient CA positively influences VCB, that is participation behavior (PB) and citizenship behavior (CB). Face gain (FG) strengthens the impact of CA and patient PB and CB, whereas face loss (FL) weakens the impact of CA and patient PB and CB. Furthermore, the fsQCA findings signify the robustness of the study model.

This study explores the multifaceted mechanism of patient value co-creation in OHC and discloses the crucial role of face for the first time. Further, the novel findings of this study provide a robust framework for advancing the understanding of important drivers of patient VCBs that significantly helps healthcare service providers and OHC managers to sustain OHCs.

This study empirically examined four value attributes (e.g. conditional value, functional value, epistemic value and price consciousness) influencing young customers’ attitudes, word-of-mouth (WOM) and continuous usage intentions. Subjective norm was positioned as the moderator, while WOM was identified as the mediator.

This study collected data from 252 Chinese young customers using purposive sampling technique and utilized PLS-SEM to examine the interrelationships among variables.

The findings confirmed that the four value attributes (i.e. conditional value, functional value, epistemic value and price consciousness) significantly influence young consumers' attitudes toward pre-made dishes (PMDs). Additionally, attitudes and WOM positively influence continuous usage intention, with WOM acting as a mediator between attitudes and continuous usage intentions. Furthermore, subjective norm partially moderates the value-attitude-behavior (VAB) model.

PMDs manufacturers should take into account the consumption values and price consciousness of young customers when developing marketing campaigns. Subjective norm and WOM continue to be key factors in enhancing continuous intention.

This study expands the applicability of the VAB model, the theory of consumption value (TCV), and the theory of planned behavior (TPB), enriching the literature on PMDs by examining four value attributes and moderating factors influencing continuous usage intention.

Integrating the Chat Generative Pre-Trained Transformer-type (ChatGPT-type) model with government services has great development prospects. Applying this model improves service efficiency but has certain risks, thus having a dual impact on the public. For a responsible and democratic government, it is necessary to fully understand the factors influencing public acceptance and their causal relationships to truly encourage the public to accept and use government ChatGPT-type services.

This study used the Latent Dirichlet allocation (LDA) model to analyze comment texts and summarize 15 factors that affect public acceptance. Multiple-related matrices were established using the grey decision-making trial and evaluation laboratory (grey-DEMATEL) method to reveal causal relationships among factors. From the two opposite extraction rules of result priority and cause priority, the authors obtained an antagonistic topological model with comprehensive influence values using the total adversarial interpretive structure model (TAISM).

Fifteen factors were categorized in terms of cause and effect, and the antagonistic topological model with comprehensive influence values was also analyzed. The analysis showed that perceived risk, trust and meeting demand were the three most critical factors of public acceptance. Meanwhile, perceived risk and trust directly affected public acceptance and were affected by other factors. Supervision and accountability had the highest driving power and acted as the causal factor to influence other factors.

This study identified the factors affecting public acceptance of integrating the ChatGPT-type model with government services. It analyzed the relationship between the factors to provide a reference for decision-makers. This study introduced TAISM to form the LDA-grey-DEMATEL-TAISM method to provide an analytical paradigm for studying similar influencing factors.