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The first purpose is to assess the quality of containerized multimodal export and the second is to develop and demonstrate the design of a service network with quality approach.

The article used the structural equation model to develop a model to measure the quality of multimodal transportation for containerized exports and finalized the model with an alternative approach. The evolutionary algorithm had been used to design a service network based on quality.

Provided factors affecting quality of multimodal transportation and reverse to one hypothesis, the construct variation in cost, time shape and quantity did not affect the quality of multimodal transportation for containerized exports. The model without variation construct was finalized by exploring causality.

This research had scope till container loading onto the vessel and assessed the quality for containerized cargo only, and second research purpose is limited by assumed values of fitness function and the limited number of nodes, in service network design demonstration.

This research provided a tool to measure the quality of multimodal transportation for containerized exports and demonstrated the field application of the model developed in service network design. This approach included all factors applicable across the container movement. The integrated approach of the article provided an organized method to design a service network for containerized exports.

This work provided the tool to assess the quality of multimodal transportation for containerized exports and developed an approach to design a service network of multimodal transportation based on quality. This approach has considered the factors of multimodal transportation comprehensively in contrast to the optimization approaches based on operation research techniques.

This work aims to examine the quality disclosure strategy of sharing economy platforms with network externality, considering consumer risk aversion.

The game theory, sensitive analysis and numerical study are used herein. The equilibria are derived from the game theory. The quality disclosure strategy is analyzed by profit comparison. To further understand the characteristics of the optimal disclosure strategy, sensitive analysis and numerical studies are conducted to detail the analytical results.

Regardless of market structure, the quality disclosure decision problem is a trade-off between information effect and cost effect. Consumer risk aversion is a factor that can incentivize low-quality platforms to disclose quality. Both consumer risk aversion and network externality influence the quality disclosure strategy through information effect. Interestingly, for different competition intensities, consumer risk aversion and network externality could lead to positive or negative information effects of removing uncertainty. The authors show that under certain conditions, consumer risk aversion and network externality could induce more quality concealment.

The quality is set exogenous herein, and the integrated process of quality investment and information disclosure is an interesting direction for future research.

This work provides managerial insights for sharing economy platforms regarding how to wisely consider consumer risk aversion and network externality when sharing quality information.

This work identifies two effects that determine quality disclosure strategy and specifies the role of each factor on quality disclosure.

In recent years, some peer-to-peer (P2P) service sharing platforms have improved their service quality by setting an entry quality threshold for service providers. Considering consumers' heterogeneous preferences for service quality and commission rate, it is worth studying how to select the commission rate contract for a P2P platform under a predetermined entry quality threshold for service providers.

In this study, the platform's profit-maximizing model is constructed under two proposed contracts: unilateral commission rate contract and bilateral commission rate contract. The optimal entry quality threshold and the optimal commission rate are obtained. This study also explores the impacts of cross-side network externality and service price on a platform's optimal decisions and social welfare.

Results show that it is always advantageous for the platform to adopt the bilateral commission rate contract, which is closely related to the strength of cross-side network externality, service price and quality sensitivity coefficient. Under certain conditions, adopting the unilateral commission rate contract can reduce platform profit and service provider surplus, and improve consumer surplus and social welfare.

This study analyzes the unilateral commission rate contract and the bilateral commission rate contract of the platform, and discusses which contract is beneficial to the platform, consumers and service providers. In addition, this study provides a basis for the operation decision of a P2P service sharing platform and the pricing decision of service providers.

Modern human society has continuous advancements that have a negative impact on the quality of the air. Daily transportation, industrial and residential operations churn up dangerous contaminants in our surroundings. Addressing air pollution issues is critical for human health and ecosystems, particularly in developing countries such as Egypt. Excessive levels of pollutants have been linked to a variety of circulatory, respiratory and nervous illnesses. To this end, the purpose of this research paper is to forecast air pollution concentrations in Egypt based on time series analysis.

Deep learning models are leveraged to analyze air quality time series in the 6th of October City, Egypt. In this regard, convolutional neural network (CNN), long short-term memory network and multilayer perceptron neural network models are used to forecast the overall concentrations of sulfur dioxide (SO₂) and particulate matter 10 µm in diameter (PM₁₀). The models are trained and validated by using monthly data available from the Egyptian Environmental Affairs Agency between December 2014 and July 2020. The performance measures such as determination coefficient, root mean square error and mean absolute error are used to evaluate the outcomes of models.

The CNN model exhibits the best performance in terms of forecasting pollutant concentrations 3, 6, 9 and 12 months ahead. Finally, using data from December 2014 to July 2021, the CNN model is used to anticipate the pollutant concentrations 12 months ahead. In July 2022, the overall concentrations of SO₂ and PM₁₀ are expected to reach 10 and 127 µg/m³, respectively. The developed model could aid decision-makers, practitioners and local authorities in planning and implementing various interventions to mitigate their negative influences on the population and environment.

This research introduces the development of an efficient time-series model that can project the future concentrations of particulate and gaseous air pollutants in Egypt. This research study offers the first time application of deep learning models to forecast the air quality in Egypt. This research study examines the performance of machine learning approaches and deep learning techniques to forecast sulfur dioxide and particular matter concentrations using standard performance metrics.

This paper aims to explore the formal (i.e. contracts, standards, processes, and structure) and informal (i.e. social structure, norms, information sharing, and value system and culture) governance instruments used in supply networks and their influence on quality.

This research is qualitative-exploratory in nature, involving semi-structured interviews with 20 managers from three essential layers in the dairy industry's supply chain: companies that supply essential inputs to milk producers; milk producers; and milk cooperatives.

Analysis of the generated data show that formal governance instruments have a strong and/or weak influence on products' and operations' quality in the dairy industry context; informal instruments have a strong and/or weak influence on quality, as a counterpart to formal instruments; and the integration of verified governance instruments positively influences the quality of products and operations.

This paper offers several managerial and practical implications. The first is to encourage suppliers of primary inputs and milk producers to invest in the formal structure, primarily in formal contracts with each other. The second implication suggests the relevance of creating different training and qualification courses with members from all organizational levels. Third, there is a need for cooperatives, encompassing all industries, to consider several informal instruments, complementary to contracts and standards currently used for processes.

Governance instruments can lead to desired supply chain outcomes, including those related to quality. Although previous supply chain studies have investigated the relationship between governance instruments and the supply chain, and quality management and the supply chain, studies on governance instruments' influence on supply chain quality are limited.

Online brand communities (OBCs) are important platforms to obtain consumers' ideas. The purpose of this study is to examine how peer influence and consumer contribution behavior simulate innovative behaviors in OBCs to increase idea quality.

Using a firm-hosted popular online brand community – Xiaomi Community (MIUI), the authors collected a set of data from 6567 consumers and then used structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to empirically test the impact of peer influence and consumer contribution behaviors on idea quality in OBCs.

The results of this study show that both peer influence breadth and depth have a positive effect on idea adoption and peer recognition, wherein proactive contribution behavior positively mediates these relationships, and responsive contribution behavior negatively mediates the impact of peer influence breadth and peer influence depth on peer recognition. A more detailed analysis using the fsQCA method further identifies four types of antecedent configurations for better idea quality.

Based on the attention-based view and the theory of learning by feedback, this study explores the factors that affect idea quality in the context of social networks and extends the research of peer influence in the digital age. The paper helps improve our understanding of how to promote customer idea quality in OBCs.

This study aims to show the effectiveness and applicability of artificial intelligence applications in the measurement and evaluation of university services. Universities can gain competitive advantage through providing their students with quality services in various aspects, such as bookstores, dormitories, recreation centers as well as cafeterias. Among these facilities, university cafeterias are places where students spend a significant amount of time. Therefore, this study aims to integrate artificial intelligence application in the evaluation of university cafeteria services based on students' perceptions with two-stage structural equation modeling (SEM) and artificial neural network (ANN) approach.

An artificial intelligence based SEM-ANN hybrid approach was used to determine the factors that have significant influence on student satisfaction, sufficiency-of-services and likelihood-of-recommendation. Data were collected from 373 students through a face-to-face questionnaire. Initially, four service quality dimensions were attained through factor analysis. Then, hypotheses, which were determined via literature review, were tested through SEM-ANN hybrid approach.

Incorporating the results of SEM analysis into the ANN technique resulted in superior models with good prediction performance. Based on four ANN models created and ANN sensitivity analyses conducted, significant predictors of satisfaction, sufficiency, reliability and recommendation are determined and ranked.

Prior studies have assessed service quality using traditional techniques, whereas, this study integrates artificial intelligence in the assessment of higher-educational institutions' services quality. Also, as a distinction from previous studies, this study ranked importance levels of predictor variables through ANN sensitivity analysis.

Drawing from social capital theory and resource dependence theory, this paper aims to test the relationship between top management team (TMT) government social capital and firm’s innovation performance via firm’s network prestige, and the moderating effect of TMT academic social capital.

The authors collected data from the China Stock Market and Accounting Research Database as well as A-share listed firms’ annual reports, and finally generated a sample of 922 firms and 2,464 firm-years from 2008 to 2014. UCINET 6.0 was used to analyze the data.

The authors find that the government social capital of TMT is positively related to firms’ innovation performance and firms’ network prestige plays a mediating role in this relationship. In addition, TMT academic social capital can strengthen the links between TMT government social capital and innovation performance through firms’ network prestige.

This paper not only contributes to literatures on the mechanism in the relationship between government social capital and firms’ innovation, but also to literatures on the effectiveness of the heterogeneity of firm’s social capital.

Although extant research on trust focuses on the dyadic relationship (trustor-trustee), the effectiveness of an employee's outcome may vary depending on the features of trust networks. This study examined how an employee's centrality in two types of trust networks (cognitive and affective) among coworkers is associated with employee job performance. Further, this study highlighted the mediating role of compassionate help in the effect of affective trust networks on individual performance.

Survey data were collected from 204 employees and 39 team leaders in South Korea. Data were analyzed using structural equation modeling.

The results indicated that cognitive trust centrality is positively associated with employee job performance, but affective trust centrality is not. However, an affective trust centrality indirectly increases individual performance via compassionate helping from coworkers.

This study contributes to a better understanding of trust networks and compassionate helping and expands both trust literature and HQR research.