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DEVELOPING AND VALIDATING A SCALE TO MEASURE FOOTBALL TEAM LOVE
No full textPrevious studies in sports marketing have shown that team love is a key factor influencing loyalty, satisfaction and positive word-of-mouth behaviours. Despite these positive findings, research on team love remains limited, and the concept’s theoretical framework is underdeveloped. The study aims to conceptualize football team love using a grounded theory approach and to develop a scale based on this conceptualization. Through a comprehensive literature review, as well as data obtained from focus group and in-depth interviews with football fans, the study delineated the unique conceptual dimensions of team love. Using data obtained from 452 Turkish football fans, exploratory factor analysis (EFA) revealed a three-dimensional (self-team integration, infinity, and passion-driven behaviours), 12-item structure. Confirmatory factor analysis (CFA) further validated and refined, a three-dimensional, nine-item measurement model. This research provides an in-depth, theory-based understanding of the unique construct of football team love, offering valuable insights for both academic and practical applications. The findings lay a foundation for further research and provide strategic guidance for sports marketers to foster stronger connections between football teams and their fans
THE IMPACT OF PERSONAL GROWTH AND HOLISTIC THINKING ON PRICE-PERCEIVED QUALITY HEURISTIC
No full textThe price-perceived quality heuristic refers to an individual\u27s predisposition to interpret price as an indicator of product quality and has been shown to be influenced by various personal factors. However, certain variables affecting this tendency has yet to be explored. This study aims to investigate whether two personal factors, namely personal growth and holistic thinking, impact this heuristic. Moreover, the study also examines the mediating roles of prestige sensitivity and risk aversion in these relationships. Data were collected using convenience sampling from 755 participants with diverse occupations across various cities and districts in Türkiye. Using structural equation modelling analyses with SPSS and AMOS software, the analyses revealed that personal growth positively influences the price-perceived quality heuristic, while holistic thinking has a negative effect. Additionally, the results confirmed the significant mediating roles of prestige sensitivity and risk aversion. These findings provide valuable insights for researchers and practitioners seeking to better understand the dynamics that shape the relationship between price and quality perception
LEAN PROCESS IMPROVEMENT IN HEALTHCARE: A STUDY USING VALUE STREAM MAPPING, THEORY OF CONSTRAINTS AND SIMULATION
No full textIn healthcare services, where human health is the primary concern, the principle of \u27\u27doing it right the first time\u27\u27 is paramount. Waste and constraints in service delivery processes can directly impact patient health outcomes. This study aim is to examine the hospitalization process of the internal medicine department of an education and research hospital using value stream mapping (VSM), theory of constraints (TOC) and simulation (using Arena software). Initially, current and future patient flows were mapped using VSM. Constraints affecting these flows were determined using TOC, while simulation was used to assess the impact of a lean model on the system proposed through the future state map (FSM). Two scenarios were developed for the future state. The findings show the presence of numerous non-value-added steps in the existing system. A leaner patient flow was achieved by minimizing these inefficiencies through the proposed future state models, addressing problematic areas that hinder the flow. Non-value-added time (NVAT) was reduced by 44 percent in the first proposed scenario and by 72 percent in the second proposed scenario. Patients\u27 length of stay (LoS) improved by 1 percent with the first model and decreased by 12 percent with the second model. Additionally, transfer time (TT) was decreased by 88 percent in the first scenario and by 92 percent in the second scenario. This study offers valuable insights and can serve as a roadmap for researchers, managers and decision-makers in the healthcare sector seeking to implement lean practices
METHOD OF PAYMENT AND ACQUIRER SHAREHOLDER WEALTH MAXIMIZATION IN MERGERS AND ACQUISITIONS: A REVIEW OF LITERATURE
No full textThis paper aims to review existing literature to study the relationship between the method of payment and the acquirer firm’s shareholder wealth in mergers and acquisitions (M&A). The literature review is carried out in two areas: (1) payment methods offered to the target firm based on the acquirer firm’s internal factors, and (2) the impact of different payment methods on shareholder wealth during the announcement period. The paper is focused on providing suggestions for future studies on the payment methods, specifically on the mixed payment method. This paper is the first attempt to critique extant literature, beginning with selecting the payment method and ending with how it would affect the acquirer firm’s shareholder wealth. Since this is a review paper and not subject to empirical study, it could be directed for future research to investigate the impact of the cash-to-stock ratio in the mixed method of payment offer on the wealth of the shareholders of the acquirer firm
Hybrid Partial Least Squares-Structural Equation Modelling and Multi-Layer Perceptron for Predicting E-Participation Success in E-Government Services: Socio-Cultural Insights and Extended Validation
No full textAlthough the Delone and McLean IS Success Model (D&M) can explain the phenomenon of e-participation success (EPS), the model was initially created in an e-commerce setting and thus neglects external factors related to e-government services. To address the gap, this study revisits the D&M by extending it with four socio-cultural constructs of Trust (TR), Anonymity (AN), Nationalism (NT), and Culture (CR). Based on 428 survey data from Malaysian citizens, a hybrid methodology was employed, integrating Partial Least Squares-Structural Equation Modelling (PLS-SEM) and Multi-layer Perceptron (MLP) to capture non-linear relationships and enhance predictive accuracy. While hybrid modelling is common, past studies have often applied limited classification metrics, and the infrequent use of comprehensive metrics, such as Area Under the Receiver Operating Characteristic Curve (AUC-ROC), may potentially affect the reliability and generalizability of these models. A comparative R² analysis between the baseline and enhanced models in this study revealed significant improvements, with R² for e-participation intention (EPI) increased from 0.620 to 0.728, EPS from 0.330 to 0.345, while User Satisfaction (US) remained strong at 0.765. The analysis predicts a 94.80% success rate for e-participation, with the MLP model further demonstrating robust classification performance, achieving an accuracy of 90.1%, a precision of 0.909, a recall of 0.948, an F1 score of 0.928, and an AUC-ROC of 0.955, outperforming other benchmark classifiers. This study contributes theoretically by introducing underexplored socio-cultural variables into the D&M while methodologically extending the hybrid PLS-SEM and MLP through a robust model validation using AUC-ROC
THE CONSTRUCTION OF THE LEX SPORTIVA PRINCIPLE IN INDONESIA’S SPORTS LAW: IMPLICATIONS AND FUTURE ARRANGEMENTS
No full textLex sportiva is a fundamental legal principle in sports law that emphasises autonomy in regulating and enforcing law in sports. The sports law in Indonesia has not yet accommodated the aspect of lex sportiva, failing to guarantee legal certainty and affecting the suboptimal regulation and enforcement of law in sports. This research aims to analyse the position of lex sportiva in sports law and the state\u27s authority in sports, as well as the implications and future regulation for strengthening this principle. This is doctrinal legal research employing conceptual and statutory approaches. The findings of this research indicate that the position of the lex sportiva principle in sports law is associated with the state\u27s authority in the field of sports, which potentially causes conflicts between laws made by a sport and by the state in organising sports. The practical implication is that implementing the lex sportiva principle in sports law may lead to legal uncertainty due to too much state intervention in sports-related arrangements in Indonesia. This research is expected to contribute to future regulatory efforts related to the affirmation of the principle in this context. Moreover, strengthening the lex sportiva principle in Sports Law and the Indonesian sports legal system can be done by revising the Sports Law and providing interpretation by the Constitutional Court, which involves the judicial review process
IMPACT OF ECONOMIC POLICY UNCERTAINTY ON HERD BEHAVIOR IN CHINA STOCK MARKET
No full textThis study explores the impact of economic policy uncertainty on herd behaviour in the Chinese stock market. As economic policy uncertainty increases, market information becomes highly chaotic and complex, making reliable information scarce and challenging for investors to make independent decisions. Particularly in the Chinese stock market, where retail investors dominate and generally lack professional financial knowledge and deep market analysis skills, these investors are more likely to mimic the behaviours of other market participants. Using monthly data from January 2011 to December 2023, and employing panel regression for empirical analysis, this research aims to explore the specific effects and mechanisms of economic policy uncertainty on herd behaviour, addressing a gap in the existing literature regarding how economic policy uncertainty directly influences investor behaviour in terms of manner and extent. The study\u27s findings indicate that economic policy uncertainty has a significant and varied impact on herding behaviour across different market segments. Specifically, economic policy uncertainty significantly promotes herding behaviour in the Science and Technology Innovation Board, while it inhibits herding behaviour in the Main Board. Economic policy uncertainty also inhibits herding behaviour in the ChiNext, but not as significantly as in the Main Board. This diversity in impact highlights the complex nature of economic policy uncertainty\u27s influence on herd behaviour
IDENTIFYING AND PRIORITIZING MARKETING PROBLEMS IN IRAN’S POULTRY INDUSTRY
No full textThe marketing of poultry products is a critical component of all production systems. Given the importance of the poultry industry in Iran and the marketing challenges it faces, this research aims to identify and prioritize the marketing problems within the industry. Using the Delphi technique, 15 experts from the poultry industry helped identify the main factors causing marketing issues in poultry units. These factors were then ranked according to their importance using the AHP method. The findings classified the main marketing problems into six categories: product, price, supply, promotion, resources, and structure. The "resources" category emerged as the highest priority, while "promotion" was deemed the lowest. Among the sub-indicators, the lack of government support and inadequate facilities from banks and related organizations ranked the highest priority, whereas insufficient advertising for chicken meat ranked the lowest. The most important innovation of this research lies in its comprehensive ranking and prioritization of marketing problems specific to the poultry industry in Iran, an area that previous research has not yet explored
Integrating Information Gain and Chi-Square for Enhanced Malware Detection Performance
No full textMalware represents a serious and continuously evolving threat in the modern digital environment. Detecting malware is essential to safeguard devices and systems from risks such as data corruption, data theft, account compromises, and unauthorized access that could result in total system takeover. As malware has progressed from its simpler, monomorphic variants to more sophisticated forms like oligomorphic, polymorphic, and metamorphic, a machine learning-based detection system is now required, surpassing the limitations of traditional signature-based methods. Recent studies have shown that this challenge can be addressed by employing machine learning algorithms for detection. Some studies have also implemented various feature selection methods to optimize detection efficiency. However, they continue to struggle with false positives and false negatives, striving to reach zero tolerance in malware detection. This study introduces the IGCS method, a combined feature selection approach that integrates Information Gain with Chi-Square (X²) to enhance both the effectiveness and efficiency of machine learning classifiers. Using IGCS, six classifiers—Random Forest, XGBoost, kNN, Decision Tree, Logistic Regression, and Naïve Bayes—achieved higher performance scores compared to other scenarios, such as when classifiers were combined with Information Gain, Chi-Square, PCA, or even without any feature selection. As a result, Random Forest with 30 features selected by IGCS proved superior to any combination of classifiers and feature selection methods in malware detection, achieving 99.0% accuracy, recall, precision, and F1-Score. This combination also demonstrated efficiency with a 52.5% decrease in training time and a 56.9% decrease in testing time
Exploring Deep Learning Techniques for Sentiment Analysis inOnline Education Platforms: A Case Study of Coursera Reviews
No full textThe rise of online courses, accelerated by the COVID-19 pandemic, has underscored the need for effective educational models capable of addressing the challenges posed by remote learning. This study focuses on the development of sentiment classifiers using the Coursera reviews dataset to evaluate the polarity of student feedback. This research improved student engagement and support in online education by applying sophisticated sentiment analysis techniques. We explored a comprehensive methodology encompassing various pre-processing techniques, advanced tokenisation methods, and a range of deep learning architectures, including Feedforward Neural Networks (FNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), and Bidirectional Encoder Representations from Transformers (BERT)-based models. Each model’s performance is optimised through meticulous hyperparameter tuning using the Optuna framework. Results indicated that BERT is the best model, achieving a recall of 97.50% and an accuracy of 96.83%, while Bidirectional LSTM (BiLSTM) closely followed with a recall of 96.55% and an accuracy of 96.71%. In contrast, simpler models like FNN and RNN exhibited lower accuracy (92.83% and 87.83%, respectively). These findings underscore the importance of advanced models in capturing contextual meanings and highlight the effectiveness of leveraging embeddings, attention mechanisms, and tailored pre-processing strategies, which significantly improve sentiment classification performance