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Firms are increasingly digitalizing their business processes and expanding them into digital platforms, which are believed to generate digital and relational resources that can facilitate and deliver innovations for firms. Instead of focusing on the extent of digital integration capability (DI), this paper seeks to empirically evaluate whether the DI asymmetry between the buyer and supplier firms influences bilateral information sharing and the buyer’s product innovation. We also examine the moderating effects of firms’ external (environmental dynamism) and internal (innovative climate) environments on these relationships.

Primary and secondary archival data on 180 buyer-supplier Chinese dyadic relationships were collected and analyzed using multiple linear regression models. Additionally, the Process macro was used to shed a nuanced light on the moderation effects of environmental dynamism and innovative climate.

The results show that DI asymmetry negatively impacts buyer firms’ product innovation through decreased information sharing. Environmental dynamism weakens the negative relationship between DI asymmetry and information sharing. Meanwhile, the innovative climate negatively moderates the relationship between information sharing and product innovation.

This study adds knowledge to the literature regarding the dark side of “one-sided digitalization.” By exploring the influences of unbalanced DI in buyer-supplier relationships, this study yields essential theoretical and managerial implications for product innovation success in a digital era.

This research addressed online customer-to-customer (C2C) incivility during digital service recovery.

To examine the effectiveness of managerial responses to online C2C incivility post a restaurant service failure, a 2 (Managerial response: general vs specific) x 2 (Failure severity: high vs low) quasi-experimental design was employed. A pretest was conducted with 123 restaurant consumers via Amazon Mechanical Turk, followed by a main study with 174 restaurant consumers. Taking a mixed-method approach, this research first asked open-ended questions to explore how participants perceived the restaurant’s motivation for providing a generic versus a specific response. Hayes’ (2013) PROCESS procedure was then performed for hypotheses testing.

The results revealed significant interaction effects of managerial responses and failure severity on perceived online service climate and revisit intention, mediated by trust with managerial responses.

This research yielded unique insight into C2C incivility management literature and industry practices in the context of digital customer service recovery.

This study aims to investigate the causal relationships among environmental, social and governance reporting (ESGR), stakeholder sustainability awareness, use of artificial intelligence (AI), sustainability culture, innovation performance and climate resilience of organizations across diverse sectors in Sri Lanka.

A survey was conducted among 327 respondents, including senior accounting professionals, operations managers and functional heads to gather company-level data in various industries in Sri Lanka. A disjoint two-stage approach validated the measurement model, and the partial least squares structural equation model (SEM) was used to test the proposed hypotheses.

The analysis evidences the mediating role of stakeholders' sustainability awareness on the relationship between ESGR and sustainability culture. Furthermore, it emphasizes the role of sustainability culture in driving climate resilience. Innovation performance acts as a moderator, strengthening the relationship between the use of AI and sustainability culture.

The study suggests that organizations should strategically use ESGR, integrate AI and prioritize stakeholder engagement to strengthen their commitment to sustainability. These provide insight for decision-making in organizations seeking to align with sustainable business practices.

It explores the use of AI to enhance ESGR and sustainability culture, providing a broader understanding of how organizations manage AI and stakeholders in sustainability issues.

To maintain the bottom line of food import risk in China, this paper proposes a novel risk state evaluation model based on bottom-line thinking after analyzing the decision-making ideas embedded in the bottom-line thinking method.

First, the order relation analysis method (G1 method) and Laplacian score (LS) are applied to calculate the constant weights of indexes. Then, the worst-case scenario of food import risk can be estimated to strive for the best result, so the penalty state variable weight function is introduced to obtain variable weights of indexes. Finally, the study measures the risk state of China's food import from the overall situation using the set pair analysis (SPA) method and identifies the key factors affecting food import risk.

The risk states of food supply in eight countries are in the state of average potential and partial back potential as a whole. The results indicate that China's food import risks are at medium and upper-medium risk levels in most years, fluctuating slightly from 2010 to 2020. In addition, some factors are diagnosed as the primary control objects for holding the bottom line of food import risk in China, including food output level, food export capacity, bilateral relationship and political risk.

This paper proposes a novel risk state evaluation model following bottom-line thinking for food import risk in China. Besides, SPA is first applied to the risk evaluation of food import, expanding the application field of the SPA method.

Educational accountability and student achievement polarization, which result in high dropout rates, pose significant challenges and pressures on teachers' pedagogical leadership. Whether pedagogical leadership, which originates in the Western educational environment, can significantly improve student achievement in Chinese high schools remains unclear. This concept has not yet been fully explored in the Chinese educational environment, and its direct impact on student achievement and the mediating role of English teaching methods remain to be investigated. However, existing research has concentrated on the effectiveness of principals' pedagogical leadership, with variations in teachers' pedagogical leadership practices. Many reform measures have been implemented in China to improve student achievement, but past educational practices have analyzed the impact on student achievement from a single instructional leadership, school capital or teaching method perspective. Furthermore, there is a lack of multidimensional and systematic assessments of the direct effects of teacher pedagogical leadership on student achievement and the mediating effects of English teaching methods.

To address this gap, this study analyzed the impact of teachers' pedagogical leadership on student achievement and the mediating effect of English teaching methods with the support of the theory of action for teacher leadership, specifically using pedagogical leadership and English teaching methods models.This study conducted a questionnaire survey of 968 participants in Taian City, China, and quantitatively analyzed the data using SmartPLS structural equation modeling (SEM).

This study revealed that pedagogical leadership has a positive direct effect on student achievement. Meanwhile, among the four mediating factors, the Direct Method, Audio-Lingual Method and Communicative Language Teaching had significant mediating effects.

This study shows that the effective use of academic and professional capital allocation in pedagogical leadership, combined with effective measures of using multiple effective English teaching methods, helps achieve high-quality student achievement.

Productivity is often cited as a key barrier to the adoption of metal laser-based powder bed fusion (ML-PBF) technology for mass production. Newer generations of this technology work to overcome this by introducing more lasers or dramatically different processing techniques. Current generation ML-PBF machines are typically not capable of taking on additional hardware to maximise productivity due to inherent design limitations. Thus, any increases to be found in this generation of machines need to be implemented through design or adjusting how the machine currently processes the material. The purpose of this paper is to identify the most beneficial existing methodologies for the optimisation of productivity in existing ML-PBF equipment so that current users have a framework upon which they can improve their processes.

The review method used here is the preferred reporting items for systematic review and meta-analysis (PRISMA). This is complemented by using an artificial intelligence-assisted literature review tool known as Elicit. Scopus, WEEE, Web of Science and Semantic Scholar databases were searched for articles using specific keywords and Boolean operators.

The PRIMSA and Elicit processes resulted in 51 papers that met the criteria. Of these, 24 indicated that by using a design of experiment approach, processing parameters could be created that would increase productivity. The other themes identified include scan strategy (11), surface alteration (11), changing of layer heights (17), artificial neural networks (3) and altering of the material (5). Due to the nature of the studies, quantifying the effect of these themes on productivity was not always possible. However, studies citing altering layer heights and processing parameters indicated the greatest quantifiable increase in productivity with values between 10% and 252% cited. The literature, though not always explicit, depicts several avenues for the improvement of productivity for current-generation ML-PBF machines.

This systematic literature review provides trends and themes that aim to influence and support future research directions for maximising the productivity of the ML-PBF machines.

The purpose of this study is to assess the incorporation of fiber reinforced polymer (FRP) bars in concrete as a reinforcement enhances the corrosion resistance in a concrete structure. However, FRP bars are not practically used due to a lack of standard codes. Various codes, including ACI-440-17 and CSA S806-12, have been established to provide guidelines for the incorporation of FRP bars in concrete as reinforcement. The application of these codes may result in over-reinforcement. Therefore, this research presents the use of a machine learning approach to predict the accurate flexural strength of the FRP beams with the use of 408 experimental results.

In this research, the input parameters are the width of the beam, effective depth of the beam, concrete compressive strength, FRP bar elastic modulus and FRP bar tensile strength. Three machine learning algorithms, namely, gene expression programming, multi-expression programming and artificial neural networks, are developed. The accuracy of the developed models was judged by R², root means squared and mean absolute error. Finally, the study conducts prismatic analysis by considering different parameters. including depth and percentage of bottom reinforcement.

The artificial neural networks model result is the most accurate prediction (99%), with the lowest root mean squared error (2.66) and lowest mean absolute error (1.38). In addition, the result of SHapley Additive exPlanation analysis depicts that the effective depth and percentage of bottom reinforcement are the most influential parameters of FRP bars reinforced concrete beam. Therefore, the findings recommend that special attention should be given to the effective depth and percentage of bottom reinforcement.

Previous studies revealed that the flexural strength of concrete beams reinforced with FRP bars is significantly influenced by factors such as beam width, effective depth, concrete compressive strength, FRP bars’ elastic modulus and FRP bar tensile strength. Therefore, a substantial database comprising 408 experimental results considered for these parameters was compiled, and a simple and reliable model was proposed. The model developed in this research was compared with traditional codes, and it can be noted that the model developed in this study is much more accurate than the traditional codes.

Early prediction of any type of cancer is important for the treatment of this type of disease, therefore, our target to evaluate whether monitoring early changes in plasma human epidermal growth factor receptor 2 (HER2) levels (using EIS), could help in the treatment of breast cancer or not? Human epidermal growth factor receptor 2 (HER2) overexpression is an important biomarker for treatment selection in earlier stages of cancers. The combined detection of the HER2 gene in plasma for blood cancer provides an important reference index for the prognosis of metastasis to other tissues. For this purpose, the authors fabricated and characterized a model wireless biosensor-based electrochemical impedance spectroscopy (EIS) for detecting HER2 plasma as therapeutics.

Most sensors generally are fabricated based on a connection between component of the sensors and the external circuits through wires. Although these types of sensors provide suitable sensitivities and also quick responses, the connection wires can be limited to the sensing ability in various devices approximately. Therefore, the authors designed a wireless sensor, which can provide the advantages of in vivo sensing and also long-distance sensing, quickly.

The biosensor structure was designed for detection of HER2, HER3 and HER-4 from lab-on-chip approach with six units of screen-printed electrode (SPE), which is built of an electrochemical device of gold/silver, silver/silver or carbon electrodes. The results exhibited that the biosensor is completely selective at low concentrations of the plasma and HER2 detection via the standard addition approach has a linearity plot, therefore, by using this type of biosensors HER2 in plasma can be detected.

This is then followed by detecting HER2 in real plasma using standard way which proved to have great linearity (R² = 0.991) proving that this technique can be used to detect HER2 solution in real patients.

Drawing from stakeholder (ST) and social exchange theory (SET), the purpose of this study is to examine the relationship between customer perceived ethicality (CPE), electronic word of mouth (eWOM), customer trust (CT) and customer loyalty (CL). Furthermore, this study aimed to understand the dual role of CPE and eWOM in obtaining CT and achieving CL.

Using a quantitative, cross-sectional research design, data were collected from face-to-face surveys, yielding 358 responses. The partial least square algorithm was used to test the proposed hypothesis.

The analysis revealed that CPE and eWOM positively affect CT and CL, and CT has a mediating effect on the association between CPE–CL and eWOM–CL. CT was also found to positively affect CL.

Hotel managers can prioritize ethical practices and leverage the power of eWOM to build trust and achieve loyalty. This integrated approach not only enhances customer satisfaction and retention but also creates a competitive advantage.

The novelty of this study lies in the investigation of the dual role played by CPE and eWOM as antecedents of CT and CL within the hotel industry. Finally, this study explains the drivers of CT and CL, thereby making a novel contribution to the literature.

With the change in climate and increased pollution, there has been a need to reduce environmental carbon emissions. This research aims to develop a framework for reducing environmental carbon footprints to improve business performance.

This study uses Scientific Procedures and Rationales for the Systematic Literature Reviews (SPAR-4-SLR) approach. Articles are searched in the Scopus database using various keywords and their combinations. It resulted in 651 articles initially. After applying different screening criteria, 61 articles were considered for the final study.

This study provided four themes and sub-themes within each category. This research also used theories, methodologies and context (TMC) framework to provide future research questions. This study used the antecedents, decisions and outcomes (ADO) framework for synthesising the findings. The ADO framework will help to achieve carbon neutrality and improve firms' supply chain (SC) performance.

This study provides theoretical implications by highlighting the various theories that can be used in future research. This study also states the practical implications for the achievement of carbon neutrality by the firms.

This study contributes to the literature linking carbon neutrality with business performance.