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This chapter of the book aims to introduce multiobjective linear programming (MLP) as an optimum tool to find the best quality engineering techniques (QET) in the main domains of supply chain management (SCM). The importance of finding the best quality techniques in SCM elements in the shortest possible time and at the least cost allows all organizations to increase the power of experts’ analysis in supply chain network (SCN) data under cost-effective conditions. In other words, this chapter aims to introduce an operations research model by presenting MLP for obtaining the best QET in the main domains of SCM. MLP is one of the most determinative tools in this chapter that can provide a competitive advantage. Under goal and system constraints, the most challenging task for decision-makers (DMs) is to decide which components to fund and at what levels. The definition of a comprehensive target value among the required goals and determining system constraints is the strength of this chapter. Therefore, this chapter can guide the readers to extract the best statistical and non-statistical techniques with the application of an operations research model through MLP in supply chain elements and shows a new innovation of the effective application of operations research approach in this field. The analytic hierarchy process (AHP) is a supplemental tool in this chapter to facilitate the relevant decision-making process.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.

The financial sector’s growing interest in leveraging artificial intelligence (AI) for forecasting has been noted in recent years. In this chapter, we delve into the application of AI in financial forecasting within Indonesia’s stock market. Our primary focus is to assess how AI’s prediction potential can impact investors and financial regulators in this context. Our review spans existing literature on AI and financial forecasting, recent developments in the Indonesian stock market, and ethical and regulatory concerns that surround AI in finance. Our analysis indicates that AI can enhance forecast accuracy in Indonesia’s stock exchange; however, we must also consider limitations and challenges.

This research navigates the confluence of data analytics, machine learning, and artificial intelligence to revolutionize the management of urban services in smart cities. The study thoroughly investigated with advanced tools to scrutinize key performance indicators integral to the functioning of smart cities, thereby enhancing leadership and decision-making strategies. Our work involves the implementation of various machine learning models such as Logistic Regression, Support Vector Machine, Decision Tree, Naive Bayes, and Artificial Neural Networks (ANN), to the data. Notably, the Support Vector Machine and Bernoulli Naive Bayes models exhibit robust performance with an accuracy rate of 70% precision score. In particular, the study underscores the employment of an ANN model on our existing dataset, optimized using the Adam optimizer. Although the model yields an overall accuracy of 61% and a precision score of 58%, implying correct predictions for the positive class 58% of the time, a comprehensive performance assessment using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metrics was necessary. This evaluation results in a score of 0.475 at a threshold of 0.5, indicating that there's room for model enhancement. These models and their performance metrics serve as a key cog in our data analytics pipeline, providing decision-makers and city leaders with actionable insights that can steer urban service management decisions. Through real-time data availability and intuitive visualization dashboards, these leaders can promptly comprehend the current state of their services, pinpoint areas requiring improvement, and make informed decisions to bolster these services. This research illuminates the potential for data analytics, machine learning, and AI to significantly upgrade urban service management in smart cities, fostering sustainable and livable communities. Moreover, our findings contribute valuable knowledge to other cities aiming to adopt similar strategies, thus aiding the continued development of smart cities globally.

HyFlex course design is an effective instructional course design that combines active and transformative learning techniques. HyFlex course design encourages active learning by focusing on interactive activities, discussions, and collaboration. It also allows learners to collaborate effectively and flexibly as a community, providing peer support and opportunities for authentic dialogue and learning experiences. HyFlex course design provides the opportunity for transformative learning through its ability to offer personalized educational experiences to individuals. It facilitates greater customization of the learning experience, allowing individual learners to access tailored educational modules, offer personalized educational experiences to individuals, and effectively develop and build independent and critical thinking skills. This conceptual review, supported by implications from HyFlex literature and triangulated with experts' views undertaking a Delphi study, facilitates understanding the current state of research in HyFlex course design and future application strategies. Existing research has identified HyFlex courses as a promising means of engaging students in active learning. Allowing students to learn through flexibly predesigned mixed online and in-person experiences enables higher levels of student autonomy and supports students in taking more ownership of their learning. This approach can facilitate an understanding of how HyFlex courses can improve active learning practices in higher education. The review study findings identify the reported alignment issues and challenges, suggest four strategies and actions for policymakers and stakeholders, and provide a suggested research agenda for bridging identified research gaps.

Future research can provide evidence of the benefits of HyFlex course design and how flexible course design can address the challenges of traditional face-to-face courses, such as reduced student engagement, lack of student-centered approaches, and limited support for different learning styles. Further research can focus on strategies that can be used to promote active learning in HyFlex courses. Moreover, research can investigate how this kind of course design can equip educators with the skills and knowledge needed to design and implement effective and meaningful active learning experiences. Finally, research can assess the potential impact of HyFlex course design on student outcomes, including performance, satisfaction, and engagement.

Design/Methodology/Approach: This chapter applies tests associated with the adaptive market hypothesis (AMH) and Johansen cointegration test. AMH acknowledges the views of the efficient market hypothesis and behavioural finance approach.

Purpose: Cryptocurrencies are considered a new asset class by multiasset portfolio managers. Hence, we examine the AMH and cointegration in the cryptocurrency market to know whether select cryptocurrencies can be diversified.

Findings: We find that cryptocurrencies are efficient and there is a long-run relationship among constituent series, and there is no short-run causality derived from bitcoin, Ethereum and litecoin to bitcoin, while stellar and Dogecoin have short-run causality to bitcoin.

Originality/Value: This chapter is different from the existing one as this is the first study in which the AMH and Johansen cointegration test are applied to check the efficiency and relationship of Bitcoin, Ethereum, and Monero, Stellar, litecoin and Dogecoin.

In this chapter we develop a new approach, based on the identification of strategy classes, to study how firms face multiple demands. The procedure that we propose (called Relational Class Analysis) stems from an analysis of the similarity of associative patterns across multiple observable outcomes, which reflect the underlying set of choices firms make to similarly address demands. Empirically, the study of 18 financial and extra-financial performance outcomes for 3,655 firms shows the existence of three main strategic classes. Drawing on our analysis, we redefine strategy as the set of committed decisions undertaken to resolve trade-offs between multiple concurrent objectives and discuss the implications of our approach for eight core questions for strategy and organizational theory.