Weiwei Wu, Zhouzhou Wang, Shuang Ding, Aiping Song and Dejia Zhu
The effects of infiltrant-related factors during post-processing on mechanical performance are fully considered for three-dimensional printing (3DP) technology. The factors…
Abstract
Purpose
The effects of infiltrant-related factors during post-processing on mechanical performance are fully considered for three-dimensional printing (3DP) technology. The factors contain infiltrant type, infiltrating means, infiltrating frequency and time interval of infiltrating.
Design/methodology/approach
A series of printing experiments are conducted and the parts are processed with different conditions by considering the above mentioned four parameters. Then the mechanical performances of the parts are tested from both macroscopic and microscopic papers. In the macroscopic view, the compressive strength of each printed part is measured by the materials testing machine – Instron 3367. In the microscopic view, scanning electron microscope and energy dispersion spectrum are used to obtain microstructure images and element content results. The pore size distributions of the parts are measured further to illustrate that if the particles are bound tightly by infiltrant. Then, partial least square (PLS) is used to conduct the analysis of the influencing factors, which can solve the small-sample problem well. The regression analysis and the influencing degree of each factor are explored further.
Findings
The experimental results show that commercial infiltrant has an outstanding performance than other super glues. The infiltrating action will own higher compressive strength than the brushing action. The higher infiltrating frequency and inconsistent infiltrating time interval will contribute to better mechanical performance. The PLS analysis shows that the most important factor is the infiltrating method. When compare the fitted value with the actual value, it is clear that when the compressive strength is higher, the fitting error will be smaller.
Practical implications
The research will have extensive applicability and practical significance for powder-based additive manufacturing.
Originality/value
The impact of the infiltrating-related post-processing on the performance of 3DP technology is easy to be ignored, which is fully taken into consideration in this paper. Both macroscopic and microscopic methods are conducted to explore, which can better explain the mechanical performance of the parts. Furthermore, as a small-sample method, PLS is used for influencing factors analysis. The variable importance in the projection index can explain the influencing degree of each parameter.
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The purpose of this study is to examine how Taylor Swift’s music influences language acquisition, gender representation, emotional well-being and cultural awareness among Thai…
Abstract
Purpose
The purpose of this study is to examine how Taylor Swift’s music influences language acquisition, gender representation, emotional well-being and cultural awareness among Thai university students within the context of lifelong learning. By exploring these dimensions, the study aims to uncover the transformative potential of integrating popular culture, particularly music, into lifelong learning frameworks, thereby offering insights into the role of music in fostering continuous education, cultural sensitivity and personal growth.
Design/methodology/approach
This qualitative study employed semi-structured interviews and focus group discussions to explore the impact of Taylor Swift’s music on language acquisition, gender representation, emotional well-being and cultural awareness within the context of lifelong learning. Around 32 university students from six prominent universities in Thailand participated in the study. Thematic analysis was used to identify and interpret the recurring themes related to how Taylor Swift’s music influences these aspects of lifelong learning, providing an in-depth understanding of her music’s role in educational and personal development.
Findings
The study found that Taylor Swift’s music significantly impacts language acquisition by providing an immersive and engaging learning environment. Her music also challenges traditional gender norms, promoting self-expression and empowerment. Additionally, the emotional resonance of her songs contributes to students' emotional well-being, offering solace and encouragement. Furthermore, her advocacy for diversity and inclusivity enhances cultural awareness, fostering empathy and cross-cultural understanding. Overall, Taylor Swift’s music serves as a powerful tool for promoting lifelong learning, cultural awareness and emotional resilience among university students.
Research limitations/implications
This study primarily focused on Thai university students, which may limit the generalizability of the findings to other cultural contexts. Future research could explore the impact of Taylor Swift’s music on a broader demographic, including different age groups and cultural backgrounds. Additionally, a longitudinal approach could provide deeper insights into how sustained engagement with music influences lifelong learning and personal development over time. The findings underscore the need for further exploration of popular culture’s role in education and its potential to enhance learning outcomes across diverse contexts.
Practical implications
The findings suggest that integrating popular music, like Taylor Swift’s, into educational curricula can enhance language learning, promote cultural awareness and support emotional well-being. Educators are encouraged to incorporate music-based activities into their teaching strategies to create a more engaging and relatable learning environment. Furthermore, the study highlights the importance of using music as a tool for challenging traditional gender norms and fostering inclusivity, suggesting that educational programs should leverage popular culture to promote social empowerment and personal growth among students.
Social implications
The study demonstrates that Taylor Swift’s music plays a significant role in promoting social empowerment, gender equality and cultural sensitivity among university students. By challenging traditional norms and advocating for inclusivity, her music encourages listeners to embrace diversity and engage in lifelong learning. The findings suggest that popular culture, particularly music, can be a powerful force for social change, fostering a more inclusive and empathetic society. This underscores the potential of music to contribute to broader social objectives, including gender equality, cultural awareness and emotional resilience.
Originality/value
This study is unique in its comprehensive examination of Taylor Swift’s music as a tool for lifelong learning among university students in Thailand. While previous research has explored music’s role in language acquisition or emotional well-being, this study integrates multiple dimensions – language learning, gender representation, emotional well-being and cultural awareness – within the context of lifelong learning. By focusing on a global pop icon’s influence in a non-Western setting, the research provides valuable insights into the transformative potential of popular culture in education, offering educators innovative strategies to engage students through music.
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Dharyll Prince Mariscal Abellana, Donna Marie Canizares Rivero, Ma. Elena Aparente and Aries Rivero
This paper aims to propose a hybrid-forecasting model for long-term tourism demand forecasting. As such, it attempts to model the tourism demand in the Philippines, which is a…
Abstract
Purpose
This paper aims to propose a hybrid-forecasting model for long-term tourism demand forecasting. As such, it attempts to model the tourism demand in the Philippines, which is a relatively underrepresented area in the literature, despite its tourism sector’s growing economic progress.
Design/methodology/approach
A hybrid support vector regression (SVR) – seasonal autoregressive integrated moving averages (SARIMA) model is proposed to model the seasonal, linear and nonlinear components of the tourism demand in a destination country. The paper further proposes the use of multiple criteria decision-making (MCDM) approaches in selecting the best forecasting model among a set of considered models. As such, a preference ranking organization method for enrichment of evaluations (PROMETHEE) II is used to rank the considered forecasting models.
Findings
The proposed hybrid SVR-SARIMA model is the best performing model among a set of considered models in this paper using performance criteria that evaluate the errors of magnitude, directionality and trend change, of a forecasting model. Moreover, the use of the MCDM approach is found to be a relevant and prospective approach in selecting the best forecasting model among a set of models.
Originality/value
The novelty of this paper lies in several aspects. First, this paper pioneers the demonstration of the SVR-SARIMA model’s capability in forecasting long-term tourism demand. Second, this paper is the first to have proposed and demonstrated the use of an MCDM approach for performing model selection in forecasting. Finally, this paper is one of the very few papers to provide lenses on the current status of Philippine tourism demand.
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Yongliang Yuan, Shuo Wang, Liye Lv and Xueguan Song
Highly non-linear optimization problems exist in many practical engineering applications. To deal with these problems, this study aims to propose an improved optimization…
Abstract
Purpose
Highly non-linear optimization problems exist in many practical engineering applications. To deal with these problems, this study aims to propose an improved optimization algorithm, named, adaptive resistance and stamina strategy-based dragonfly algorithm (ARSSDA).
Design/methodology/approach
To speed up the convergence, ARSSDA applies an adaptive resistance and stamina strategy (ARSS) to conventional dragonfly algorithm so that the search step can be adjusted appropriately in each iteration. In ARSS, it includes the air resistance and physical stamina of dragonfly during a flight. These parameters can be updated in real time as the flight status of the dragonflies.
Findings
The performance of ARSSDA is verified by 30 benchmark functions of Congress on Evolutionary Computation 2014’s special session and 3 well-known constrained engineering problems. Results reveal that ARSSDA is a competitive algorithm for solving the optimization problems. Further, ARSSDA is used to search the optimal parameters for a bucket wheel reclaimer (BWR). The aim of the numerical experiment is to achieve the global optimal structure of the BWR by minimizing the energy consumption. Results indicate that ARSSDA generates an optimal structure of BWR and decreases the energy consumption by 22.428% compared with the initial design.
Originality/value
A novel search strategy is proposed to enhance the global exploratory capability and convergence speed. This paper provides an effective optimization algorithm for solving constrained optimization problems.
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Arpita Agnihotri and Saurabh Bhattacharya
Case can be taught at the undergraduate or postgraduate level, including executive Master of Business Administration programs.
Abstract
Study Level/Applicability
Case can be taught at the undergraduate or postgraduate level, including executive Master of Business Administration programs.
Subject Area
This case is intended for courses in strategic management, entrepreneurship and innovation at the undergraduate or postgraduate level.
Case Overview
The case is about challenges faced by Linda Portnoff, the Co-founder and Chief Executive Officer of Riteband, a Sweden-based fintech startup. In March 2020, Portnoff was conducting beta testing of Riteband’s app, which experts considered the world’s first stock exchange for music trading. After completing a PhD, Portnoff who was working as a Research Analyst, left her job to pursue entrepreneurship. Through Riteband, Portnoff helped to resolve pain points of artists who were forced to give the copyright of their music tracks or albums to distributors, in lieu of funds or promotional campaigns that distributors arranged for them. Portnoff invested in developing a patent-pending machine learning-based algorithm that based on several parameters could predict the likelihood of a music track or an album to become a success. Based on this prediction and royalty that artists were interested in sharing with fans, shares were issued to investors, who were also fans of the artists. As Portnoff identified an innovative business opportunity to trade music on a stock exchange based on Riteband’s machine learning algorithm, competition in Riteband’s strategic group was also becoming intense. Consequently, Portnoff was facing challenges of establishing competitive advantage of Riteband. Furthermore, as women in general faced challenges in raising funds for their startups, and even though Portnoff obtained some funding for Riteband, but overall, funding was a challenge for her as well. Moreover, as machine learning was a technical aspect for artists and potential investors, Portnoff also faced challenges to monetize on its machine learning algorithm.
Expected learning outcomes
By the end of the case study discussion, students should be able to: understand the principles of cross-industry innovation and explain the creation of new business opportunities based on cross-industry innovation; differentiate between direct and indirect competitors through strategic group analysis and further critically analyze the competitive advantage of business over other direct competitors; determine ways of reducing gender biases in venture capital funding; describe how machine learning works and further formulate ways to monetize a business through machine learning; and demonstrate the application of the value proposition canvas and business model canvas.
Subject codes
CSS 3: Entrepreneurship; CSS 11: Strategy.
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Wenxia Ge, Tony Kang, Gerald J. Lobo and Byron Y. Song
The purpose of this paper is to examine how a firm’s investment behavior relates to its subsequent bank loan contracting.
Abstract
Purpose
The purpose of this paper is to examine how a firm’s investment behavior relates to its subsequent bank loan contracting.
Design/methodology/approach
Using a sample of US firms during the period 1992-2011, the authors examine the association between overinvestment (underinvestment) and three characteristics of bank loan contracts: loan spread, collateral requirement, and loan maturity.
Findings
The authors find that overinvesting firms obtain loans with higher loan spreads. Additional tests show that the effect of overinvestment on loan spreads is generally more pronounced in firms with lower reputation, weaker shareholder rights, and lower institutional ownership. The effect of overinvestment on collateral requirement is mixed, and investment efficiency has no significant relation to loan maturity.
Research limitations/implications
The results are subject to the following caveats. First, while the study provides empirical evidence that investment efficiency affects bank loan contracting terms, especially the cost of bank loans, the underlying theory is not well-developed. The authors leave it up to future research to provide a theoretical framework to clearly distinguish the cash flow and credit risk effects of past investment behavior from those of existing agency conflicts. Second, due to data limitation, the sample size is small, especially when the authors control for corporate governance measured by G-index and institutional ownership.
Practical implications
The finding that overinvestment is costly to corporations suggests that managers should consider the potential trade-offs from such investment decisions carefully. The evidence also alerts shareholders and board members to the importance of monitoring management investment decisions. In addition, the authors find that corporate governance moderates the relationship between investment decisions and cost of bank loans, suggesting that it would be beneficial to design effective governance mechanisms to prevent management from empire building and motivate managers to pursue efficient investment strategies.
Originality/value
First, the findings enhance understanding of the potential economic consequences of overinvestment decisions in the context of a firm’s private debt contracting. The evidence suggests that lenders perceive higher credit risk from overinvestment than from underinvestment, likely because firms squander cash in the current period by investing in (negative net present value) projects that are likely to result in future cash flow problems. Second, the study contributes to the literature on the determinants of bank loans by identifying an observable empirical proxy for uncertainty in future cash flows that increases credit risk.
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This study aims to provide a comprehensive review of hotel demand forecasting to identify its key fundamentals and evolution and future research directions and trends to advance…
Abstract
Purpose
This study aims to provide a comprehensive review of hotel demand forecasting to identify its key fundamentals and evolution and future research directions and trends to advance the field.
Design/methodology/approach
Articles on hotel demand modeling and forecasting were identified and rigorously selected using transparent inclusion and exclusion criteria. A final sample of 85 empirical studies was obtained for comprehensive analysis through content analysis.
Findings
Synthesis of the literature highlights that hotel forecasting based on historical demand data dominates the research, and reservation/cancellation data and combined data gradually attracted research attention in recent years. In terms of model evolution, time series and AI-based models are the most popular models for hotel demand forecasting. Review results show that numerous studies focused on hybrid models and AI-based models.
Originality/value
To the best of the authors’ knowledge, this study is the first systematic review of the literature on hotel demand forecasting from the perspective of data source and methodological development and indicates future research directions.
目的
本研究旨在对酒店需求预测进行全面回顾, 以确定其关键基础和演变以及未来的研究方向和趋势, 以推动该领域的发展。
设计/方法/方法
使用严格和透明的纳入和排除的标准对酒店需求建模和预测的文章进行识别和选择。通过内容分析, 最终有 85个实证研究作为综合分析的样本。
研究结果
综合文献发现, 基于历史需求数据的酒店预测在研究中占主导地位, 近年来预订/取消数据和组合数据逐渐引起研究关注。在模型演化方面, 时间序列和基于人工智能的模型是最受欢迎的酒店需求预测模型。审查结果表明, 许多研究都集中在混合模型和基于 AI 的模型上。
原创性/价值
本研究是第一次从数据源和方法发展的角度对酒店需求预测文献进行系统回顾, 并指出未来的研究方向。
Propósito
Este estudio tiene como objetivo proporcionar una revisión amplia de la previsión sobre la demanda hotelera a la hora de identificar sus fundamentos clave, la evolución y las direcciones y tendencias de investigación futuras para avanzar en el campo de estudio.
Diseño/metodología/enfoque
Se identificaron y seleccionaron de forma rigurosa artículos sobre modelado y previsión de la demanda hotelera utilizando criterios transparentes de inclusión y exclusión. Se obtuvo una muestra final de 85 estudios empíricos para su análisis integral a través del análisis de contenido.
Hallazgos
La síntesis de la literatura destaca que la previsión hotelera basada en datos históricos de demanda ha dominado la investigación, y los datos de reserva/cancelación, así como los datos combinados han atraído gradualmente en los últimos años la atención de la investigación. En términos de evolución del modelo, las series temporales y los modelos basados en IA son los modelos más populares para la previsión de la demanda hotelera. Los resultados de la revisión muestran que numerosos estudios se han centrado en modelos híbridos y basados en IA.
Originalidad/valor
Este estudio es la primera revisión sistemática de la literatura sobre la previsión de la demanda hotelera desde la perspectiva de la fuente de datos y el desarrollo metodológico e indica futuras líneas de investigación.
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Mahanish Panda, Munshi Maksud Hossain, Roma Puri and Anees Ahmad
Artificial intelligence (AI) has transformed various sectors, including automotive, finance, media, travel and retail by leveraging new-age technologies. Education, banking…
Abstract
Purpose
Artificial intelligence (AI) has transformed various sectors, including automotive, finance, media, travel and retail by leveraging new-age technologies. Education, banking, health care, social policy and regulation, within the public sector have witnessed significant AI applications and substantial benefits. The importance of AI in the public sector includes enhanced efficiency, improved decision-making, cost savings, citizen-centric services, etc. Despite these advancements, a mindful discussion on the societal impact of AI in the public sector demands comprehension regarding its subjugation. Therefore, this study aims to analyze the role of AI in transforming the public sector using a bibliometric analysis of recent trends and challenges.
Design/methodology/approach
This study has used bibliometric analysis to trace the intellectual patterns of previous research. It comprises 231 articles from 2000 to 2024 from Scopus through the Scientific Procedures and Rationales for Systematic Literature Reviews protocol. This protocol has adopted a three-step process for identifying articles, i.e. assembling, arranging and assessing.
Findings
The publication trend shows an upward trajectory since 2017, whereas network visualization protrudes with the recent trends and thematic expressions, namely, Global AI ethics and policy challenges in public sectors, AI adoption and governance in public sector, challenges and opportunities of implementing AI in public administration and AI’s role in economic and public transformation.
Research limitations/implications
The findings suggest AI adoption in the public sector enhances transparency and efficiency but demands ethical guidelines, legal frameworks and stakeholder governance to address challenges such as data privacy, algorithmic bias and public trust. Policies should promote responsible AI use, balancing innovation with accountability to improve public service delivery and uphold democratic values.
Originality/value
This paper enhances the limited literature on the integration of AI in the public sector, focusing on emerging themes and trending topics with future research directions to furnish a holistic perspective. It aims to guide researchers and policymakers in exploring areas for further investigation in this domain.
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Jeongjoon Boo, Seung Yeob Lee and Byung Duk Song
The next generation of mobility is arising, and various challenging mobilities have entered the limelight. One of the most exciting of these is urban air mobility (UAM), and one…
Abstract
Purpose
The next generation of mobility is arising, and various challenging mobilities have entered the limelight. One of the most exciting of these is urban air mobility (UAM), and one of its challenges is constructing effective and efficient UAM service network. This study took a quantitative approach to the problem in an effort to support and facilitate the UAM service industry.
Design/methodology/approach
This study derived a multi-objective and multi-period (MOMP) location optimization model to support strategic UAM service network design. The model, based on its long-term service plan, determines where and when to open UAM airports. In addition, this study applied a modified e-constraint algorithm to derive managerial decisions on the Pareto relationship in consideration of multiple objectives and multiple periods.
Findings
Each Pareto solution represents a different UAM service network configuration. Thus, the model can analyze the trade-offs between Pareto decisions for the UAM service network. A case study of UAM service network design in South Korea demonstrates the validity of the proposed mathematical model and algorithm.
Practical implications
The design of a UAM service network should consider various aspects. Its construction and operation would require significant investments of time, capital and people, which would redound to society over a significant span of time. The results of this study provide quantitative guidelines for derivation and analysis of various UAM service network configurations in consideration of multiple objectives and multiple periods.
Originality/value
This paper proposes MOMP optimization, which approach is suitable to the fundamental characteristics of expanding UAM service networks and their design. It is expected that the present study will make significant contributions to the efforts of those deriving and analyzing future UAM service networks.
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Yuling Wei, Jhanghiz Syahrivar and Attila Endre Simay
Chatbots have been explored as a novel approach to enhancing consumer engagement by delivering more enjoyable, personalized services. This research aims to investigate the…
Abstract
Purpose
Chatbots have been explored as a novel approach to enhancing consumer engagement by delivering more enjoyable, personalized services. This research aims to investigate the mechanism through which anthropomorphic elements of chatbots influence consumers' intentions to use the technology.
Design/methodology/approach
This research introduces five key concepts framed through the “computers-are-social-actors” (CASA) paradigm: form realism (FR), behavioral realism (BR), cognitive trust (CT), entertainment (EM) and chatbot usage intention (CUI). An online questionnaire garnered 280 responses from China and 207 responses from Indonesia. Data collection employed a combination of purposive and snowball sampling techniques. This research utilized structural equation modeling through the analysis of moment structures (AMOS) 27 software to test the hypotheses.
Findings
(1) FR positively predicts CT and EM, (2) FR negatively predicts CUI, (3) BR positively predicts CT and EM, (4) BR positively predicts CUI and (5) Both CT and EM mediate the relationship between FR and CUI, as well as between BR and CUI.
Originality/value
This research enriches the current literature on interactive marketing by exploring how the anthropomorphic features of chatbots enhance consumers' intentions to use such technology. It pioneers the exploration of CT and EM as mediating factors in the relationship between chatbot anthropomorphism and consumer behavioral intention. Moreover, this research makes a methodological contribution by developing and validating new measurement scales for measuring chatbot anthropomorphic elements.