Kybernetes最新文献

Purpose

This paper aims to study the establishment of cooperative supply game model considering transportation hub location, and design the profit allocation rule of the cooperative supply coalition.

Design/methodology/approach

Based on the economic lost-sizing (ELS) game model and considering the location of transportation hub and the topology design of basic traffic network, we build a supply game model to maximize the profit of cooperative supply coalition. Based on the principle of proportion and the method of process allocation, we suppose the procedural proportional solution of the supplier cooperative supply game.

Findings

Through numerical examples, the validity and applicability of the proposed model and the procedural proportional solution were verified by comparing the procedural proportional solution with the weighted Shapley value, the equal division solution and the proportional rule.

Originality/value

This paper constructs a feasible mixed integer programming model for cooperative supply game. We also provide the algorithm of the allocation rule of cooperative supply game and the property analysis of the allocation rule.

Purpose

Once regional financial risks erupt, they not only affect the stability and security of the financial system in the region, but also trigger a comprehensive financial crisis, damage the national economy, and affect social stability. Therefore, it is necessary to regulate regional financial risks through artificial intelligence methods.

Design/methodology/approach

In this manuscript, we scrutinize the loan data pertaining to aggregated regional financial risks and proffer an ARIMA-SVR loan data regression model, amalgamating traditional statistical regression methods with a machine learning framework. This model initially employs the ARIMA model to accomplish historical data fitting and subsequently utilizes the resultant error as input for SVR to refine the non-linear error. Building upon this, it integrates with the original data to derive optimized prediction results.

Findings

The experimental findings reveal that the ARIMA-SVR (Autoregress Integrated Moving Average Model-Support Vector Regression) method advanced in this discourse surpasses individual methods in terms of RMSE (Root Mean Square Error) and MAE (Mean Absolute Error) indices, exhibiting superiority to the deep learning LSTM method.

Originality/value

An ARIMA-SVR framework for the financial risk recognition is proposed. This presentation furnishes a benchmark for future financial risk prediction and the forecasting of associated time series data.

Purpose

The Multi-Stakeholder Recommendation System learns consumer and producer preferences to make fair and balanced recommendations. Exclusive consumer-focused studies have improved the recommendation accuracy but lack in addressing producers' priorities for promoting their diverse items to target consumers, resulting in minimal utility gain for producers. These techniques also neglect latent and implicit stakeholders' preferences across item categories. Hence, this study proposes a personalized diversity-based optimized multi-stakeholder recommendation system by developing the deep learning-based diversity personalization model and establishing the trade-off relationship among stakeholders.

Design/methodology/approach

The proposed methodology develops the deep autoencoder-based diversity personalization model to investigate the producers' latent interest in diversity. Next, this work builds the personalized diversity-based objective function by evaluating the diversity distribution of producers' preferences in different item categories. Next, this work builds the multi-stakeholder, multi-objective evolutionary algorithm to establish the accuracy-diversity trade-off among stakeholders.

Findings

The experimental and evaluation results over the Movie Lens 100K and 1M datasets demonstrate that the proposed models achieve the minimum average improvement of 40.81 and 32.67% over producers' utility and maximum improvement of 7.74 and 9.75% over the consumers' utility and successfully deliver the trade-off recommendations.

Originality/value

The proposed algorithm for measuring and personalizing producers' diversity-based preferences improves producers' exposure and reach to various users. Additionally, the trade-off recommendation solution generated by the proposed model ensures a balanced enhancement in both consumer and producer utilities.

Purpose

Tourism is an essential industry in Indonesia, and understanding its inter-sectoral and inter-regional connections is critical for policy development. This study examines the economic impact of regional tourism in Indonesia and the connections between different tourism-related regions and industries.

Design/methodology/approach

This study uses a non-survey method to estimate the inter-regional input-output table (IRIOT) in 2019, backward and forward linkage to identify the role of tourism in the economy, and the structural path analysis (SPA) to identify the inter-sectoral and inter-regional flow of tourism effect. The benchmark IRIOT 2016 published by Badan Pusat Statistik (BPS) serves as the primary data source.

Findings

The findings indicate that tourism has a relatively high impact on the overall national economy and plays an essential role in nine provinces. However, this study uses four provinces to represent Indonesian tourism: Jakarta, Jawa Timur, Bali, and Kepulauan Riau. The SPA result captures that Kepulauan Riau Province has the highest tourism multiplier effect and Jawa Timur has the highest coverage value. Moreover, the manufacturing sector receives the most benefit from the tourism effect, followed by trade, construction, agriculture, transportation, and electricity-gas. From a spatial perspective, tourism connections are not solely based on geographical proximity. Instead, they are established through an intricate supply chain network of manufactured goods. This emphasizes the significance of considering supply chain dynamics when investigating inter-regional relationships in the tourism sector.

Originality/value

This research contributes to the literature by estimating the IRIOT in 2019, disaggregating tourism activities from related economic sectors, constructing tourism-extended IRIOT, and identifying the critical path of tourism effect in numerous provinces with different economic structures. This novel approach offers valuable insights into the full spectrum of tourism’s economic impact, which has not been previously explored in this depth. This study is useful for policymaking, investment insight, and disaster mitigation.

Purpose

This article examines the concepts of “deglobalization” and “decoupling” from the perspectives of developing and developed nations. It also assesses the short-term impacts of globalization, particularly in the context of the COVID-19 pandemic and predicts the long-term effects on global trade and cooperation between nations.

Design/methodology/approach

Panel data from 85 countries (2000–2022) were utilized. Poisson Pseudo-Maximum Likelihood (PPML) regression analysis was conducted to analyze pre- and post-COVID-19 globalization levels. The analysis focuses on trade patterns and trends, specifically comparing the effects on developing and developed nations.

Findings

First, there was a slight decline in global trade in 2020 due to COVID-19, followed by recovery in 2021–2022. Second, developing nations experienced more significant trade declines than did developed nations. Third, while US? China trade decreased slightly, China-India and US-India trade increased during the pandemic. These findings suggest that while there may be short-term disruptions, long-term trends indicate resilience in global trade patterns, with shifts in output and new partnerships emerging.

Originality/value

This study contributes to the understanding of deglobalization and decoupling by providing empirical evidence on pre- and post-COVID-19 trade patterns. The findings suggest that while globalization may have short-term effects, it is likely to lead to post-pandemic recovery and strengthened cooperation between developing and developed nations. This research also highlights the importance of developing strategies to manage uncertainty and external shocks in global trade, emphasizing the role of lockdown measures, national security considerations, and trade policies in shaping the future of globalization and decoupling.

Purpose

This study aims to deepen the understanding of consumer engagement and satisfaction within the health and wellness tourism sector, a rapidly growing niche in the global tourism industry. It focuses on identifying key elements that influence consumer perceptions and experiences in this domain.

Design/methodology/approach

Employing a quantitative approach, this research utilizes Dynamic Correlated Topic Models (DCTM) and sentiment analysis techniques to analyze user-generated content from TripAdvisor. The methodology involves parsing through extensive online reviews to extract thematic patterns and emotional sentiments related to various wellness tourism experiences.

Findings

The findings reveal that wellness and relaxation, spa and therapy services, and cultural immersion are significant factors influencing consumer satisfaction in health and wellness tourism. These elements contribute to a more profound and emotionally satisfying tourist experience, highlighting the shift from traditional tourism to more holistic, wellness-focused travel.

Research limitations/implications

The study is limited by its focus on user-generated content from a single platform, which may not fully represent the diverse range of consumer experiences in health and wellness tourism. Future research could expand to include other platforms and cross-reference with qualitative data.

Practical implications

The study offers valuable implications for destination managers and marketers in the health and wellness tourism industry, suggesting that enhancing and promoting wellness-centric experiences can significantly improve consumer satisfaction and engagement.

Social implications

The research underscores the growing importance of health and wellness in societal values, reflecting a shift in consumer preferences towards travel experiences that offer mental, physical, and spiritual benefits. This has broader implications for how destinations can cater to the evolving demands of socially conscious travelers.

Originality/value

This research contributes original insights into the evolving field of health and wellness tourism by integrating advanced text mining techniques to analyze consumer feedback, offering a novel perspective on what drives engagement and satisfaction in this sector.

Purpose

This study aimed to examine how knowledge sharing, knowledge management, supply chain efficiency and integration collectively impacted firm performance. Additionally, it investigated the moderating influence of reverse logistics on these relationships, seeking to enhance understanding of the complex dynamics within organizations.

Design/methodology/approach

A comprehensive method was used in the research design, combining a thorough evaluation of the body of literature with organized questionnaire data collection. Random sampling was used to collect data from Pakistani manufacturing companies, and PLS-SEM was used to analyze the collected data.

Findings

The findings demonstrated the strong positive relationships between knowledge management, integration, supply chain effectiveness, and information sharing and business performance. The study also showed that reverse logistics improved and moderated these correlations, highlighting the significance of managing reverse logistics well for the best possible company performance.

Practical implications

In terms of practical implications, the study offered organizations looking to improve performance useful information. Making informed strategic decisions was made possible by realizing the benefits of knowledge management, integration, supply chain efficiency, and sharing. The relevance of using successful tactics to maximize company outcomes was highlighted by highlighting the moderating effects of reverse logistics.

Originality/value

By thoroughly analyzing the connections between knowledge management, supply chain effectiveness, integration, and firm performance—while taking into account the moderating influence of reverse logistics—this study enhanced the body of existing literature. The discoveries significantly added value to this research topic by enhancing our understanding of how these elements collectively influence business performance, especially in the sometimes disregarded field of reverse logistics.

Purpose

The aim of this study is to develop a new multidimensional index to measure export potential of textile firms by using firm-level data.

Design/methodology/approach

After a conceptual model, a structural equation model is developed with five dimensions and 27 observed variables based on resource-based view theory. The measurement model is solved by Linear Structural Relations (LISREL) with maximum likelihood algorithm by using data collected from 454 textile firms in Türkiye.

Findings

In this study, a new multidimensional index that measures export potential of textile firms is developed. With the proposed model, the export potential of textile firms can be calculated numerically with the five dimensions: Resources, Dynamism, Knowledge, Innovation and Sustainability. The comparison of the output of the proposed model with the control variable, firm’s actual export values, shows a significantly high success ratio of 90.76%.

Research limitations/implications

The model is applicable for textile firms at different export levels, regions and sub-sectors. The Export Potential Index for Textile Industry model is verified by using Turkish textile industry data. The robustness of the model may be increased by verifying the model by using some other countries data. This model can be implemented to other industrial sectors with some modification of the dimensions and variables.

Practical implications

The proposed model will contribute to the firms by calculating their export potential in five dimensions with their own variables numerically. The model will help firms to develop strategies to increase their export potential and to the governmental and industrial organizations to develop incentives policies.

Originality/value

This paper fills the gap in the literature by proposing a multidimensional index that determines a firm’s export potential numerically by using firm-level data.

Purpose

The primary aim of this study is to provide actionable guidance for augmenting profitability in photovoltaic power plant investments within Iran’s solar energy sector. By emphasizing prudent capital management and strategic investment decisions, our research seeks to assist emerging businesses in attaining sustained success in this domain.

Design/methodology/approach

This study presents a comprehensive approach to refined decision-making in Iran’s solar energy sector. Our methodology integrates the best-worst method, ArcGIS software for site selection, and the TOPSIS method for decision-making, aiming to enhance precision and reliability.

Findings

Our research has identified ten promising regions suitable for photovoltaic power plant installations in Iran. Leveraging the TOPSIS method, we have made optimal selections among these alternatives. Furthermore, our exhaustive cost analysis, incorporating factors like land prices, system maintenance, revenue estimation, and various financial scenarios, has yielded insights into project cost-effectiveness.

Originality/value

By filling a notable gap in the literature regarding optimal site selection and investment strategies for photovoltaic power plants in Iran, our research contributes to the sustainable development of solar energy infrastructure. Through a thorough literature review and the development of a novel methodology, we offer valuable guidance for businesses and investors seeking success in Iran’s solar energy sector. Our study represents a significant advancement by introducing a novel methodology that integrates the best-worst method, ArcGIS software, and the TOPSIS method for site selection and investment analysis. These findings furnish valuable guidance for businesses seeking success in the solar energy sector, thereby contributing to the sustainable development of renewable energy infrastructure in Iran and beyond.

Purpose

Public reviews on educational robots are of great importance for the design, development and management of the most advanced robots with an educational purpose. This study explores the public attitudes and emotions toward educational robots through online reviews on Weibo and Twitter by using text mining methods.

Design/methodology/approach

Our study applied topic modeling to reveal latent topics about educational robots through online reviews on Weibo and Twitter. The similarities and differences in preferences for educational robots among public on different platforms were analyzed. An enhanced sentiment classification model based on three-way decision was designed to evaluate the public emotions about educational robots.

Findings

For Weibo users, positive topics tend to the characteristics, functions and globalization of educational robots. In contrast, negative topics are professional quality, social crisis and emotion experience. For Twitter users, positive topics are education curricula, social interaction and education supporting. The negative topics are teaching ability, humanistic care and emotion experience. The proposed sentiment classification model combines the advantages of deep learning and traditional machine learning, which improves the classification performance with the help of the three-way decision. The experiments show that the performance of the proposed sentiment classification model is better than other six well-known models.

Originality/value

Different from previous studies about attitudes analysis of educational robots, our study enriched this research field in the perspective of data-driven. Our findings also provide reliable insights and tools for the design, development and management of educational robots, which is of great significance for facilitating artificial intelligence in education.