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Following behavioral finance and monetary wisdom, the authors theorize: Decision-makers (investors) adopt deep-rooted personal values (the love-of-money attitudes/avaricious financial aspirations) as a lens to frame critical concerns (short-term and long-term investment decisions) in the immediate-proximal (current income) and distal-omnibus (future inheritance) contexts to maximize expected utility and ultimate serenity across context, people and time.

The authors collected data from 277 active equity traders (professional money managers and individual investors) in Pakistan’s two most robust investment hubs—Karachi and Lahore. The authors measured their love-of-money attitude (avaricious monetary aspirations), short-term and long-term investment decisions and demographic variables and collected data during Pakistan's bear markets (Pakistan Stock Exchange, PSX-100).

Investors’ love of money relates to short-term and long-term decisions. However, these relationships are significant for money managers but non-significant for individual investors. Further, investors’ current income moderates this relationship for short-term investment decisions but not long-term decisions. The intensity of the aspirations-to-short-term investment relationship is much higher for investors with low-income levels than those with average and high-income levels. Future inheritance moderates the relationships between aspirations and short-term and long-term decisions. Regardless of their love-of-money orientations, investors with future inheritance have higher magnitudes of short-term and long-term investments than those without future inheritance. The intensity of the aspirations-to-investments relationship is more potent for investors without future inheritance than those with inheritance. Investors with low avaricious monetary aspirations and without inheritance expectations show the lowest short-term and long-term investment decisions. Investors' current income and future inheritance moderate the relationships between their love of money attitude and short-term and long-term decisions differently in Pakistan's bear markets.

The authors help investors make financial decisions and help financial institutions, asset management companies, brokerage houses and investment banks identify marketing strategies and investor segmentation and provide individualized services.

Professional money managers have a stronger short-term orientation than individual investors. Lack of wealth (current income and future inheritance) motivates greedy investors to take more risks and become more vulnerable than non-greedy ones—investors’ financial resources and wealth matter. The Matthew Effect in investment decisions exists in Pakistan’s emerging economy.

This paper aims to evaluate the progress made in achieving sustainable development goal-2 (SDG 2) in India, with a focus on ending hunger, ensuring food security, improving nutrition and promoting sustainable agriculture. The assessment uses data from SDG Index reports, which offer a comprehensive overview of the advancements made by 28 states and 8 union territories (UTs) in India.

The evaluation is based on information derived from three editions of the SDG Index reports, initially published in 2018 and subsequently in 2019 and 2020. These reports provide a detailed analysis of the status and achievements of different states and UTs in relation to SDG 2. The categorization of states and UTs into aspirant, performer, front runner and achiever categories serves as a crucial framework for assessing the progress.

Despite concerted efforts by India, the majority of states and UTs are positioned in the aspirant and performer categories, suggesting that significant challenges persist in achieving SDG 2 targets. The results emphasize the necessity for stronger measures to elevate states and UTs to the categories of front-runners and achievers. The persistent challenges of malnutrition, hunger and their economic ramifications require immediate and strategic interventions to address these pressing concerns.

This paper contributes to the existing literature by providing a comprehensive analysis of the progress towards SDG 2 in India, using the insights from the SDG Index reports. The categorization framework used in this assessment offers a nuanced understanding of the challenges faced by different regions, highlighting the original contribution of this study. The findings underscore the urgency of targeted efforts to address malnutrition, hunger and related issues, emphasizing the importance of sustained commitment to achieving SDG 2 for the overall well-being of vulnerable populations.

This study aims to progress scholarly understanding of the staycation phenomenon by examining customer segments and documenting local customers’ attribute preferences.

A stated choice experiment is used to examine customer preferences for staycation package attributes. Latent class discrete choice modeling is deployed to classify customers into market segments based on their preferences. The profile of each segment is enhanced by documenting customer characteristics and consumption styles.

Six prominent market segments are identified using a combination of sociodemographics, consumption styles and staycation attribute preferences. The findings draw on consumer experiences during the COVID-19 pandemic to generate theoretical insights into preferred staycation packages. Empirically, the estimation results from the research framework and choice experimental method demonstrate that staycation market segments exhibit distinct preference structures.

Practitioners and policymakers can incorporate the findings of this study in designing and/or assessing staycation packages. This can ensure differentiated products for defined segments that resonate within local communities through positive word of mouth, thus offering prospective spillovers to visiting friends and relatives.

This is a pioneering study on preference heterogeneity from the customer perspective, with a focus on staycation markets. The findings can encourage and assist hotel sector leaders to capitalize on local market developments to achieve a more resilient hospitality business model.

The present study integrates inclusive leadership and protection motivation theory to propose a new model predicting employees' intention to work from home during an emergency situation such as the COVID-19 pandemic.

A questionnaire was developed to collect data from 939 Taiwanese and Vietnamese office employees using a non-probability convenience sampling method. A total of 887 valid questionnaires were used for further analysis. The data were analysed following a two-stage structural equation modelling using SPSS 22 and AMOS 20 software. The validity and reliability of the instrument were tested and ensured.

The results revealed that inclusive leadership and factors related to protection motivation theory– including perceived severity and perceived vulnerability – have positive direct and indirect effects on employees' work-from-home intentions through the mediating role of employees' work-from-home-related attitudes. Protection motivation theory factors were found to have a stronger effect on employees' work-from-home intention than inclusive leadership. Differences in the relationship between perceived vulnerability, perceived severity and employees' intentions towards working from home were also discovered among participants from the two studied countries.

The integration of inclusive leadership and protection motivation theory brings into light what will drive employees' intention to work from home during an emergency situation. The present study has several theoretical and practical implications for scholars, governments, managers and policymakers that can help them improve management policies for working from home in the future.

Based on integrating inclusive leadership and protection motivation theory to explore employees' intention to work from home during an emergency situation, the present study demonstrated that inclusive leadership and protection motivation theory should be considered for studies on working from home in a pandemic setting.

This study seeks to explore the intricate relationship among supply chain transparency, digitalization and idiosyncratic risk, with a specific focus on newly public firms. The objective is to determine whether supply chain transparency effectively mitigates idiosyncratic risk within this context and to understand the potential impact of digitalization on this dynamic interplay.

The study utilizes data from Initial Public Offerings (IPOs) on China’s Growth Enterprise Board (ChiNext) over the last five years, sourced from the CSMAR database and firms’ annual reports. The research covers the period from 2009 to 2021, observing each firm for five years post-IPO. The final sample comprises 2,645 observations from 529 firms. The analysis employs the Hausman test, considering the panel-data structure of the sample and favoring fixed effects over random effects. Additionally, it applies the high-dimensional fixed effects (HDFE) estimator to address unobserved heterogeneity.

The analysis initially uncovered an inverted U-shaped relationship between supply chain transparency and idiosyncratic risk, indicating a delicate equilibrium where detrimental effects diminish and beneficial effects accelerate with increased transparency. Moreover, this inverted U-shaped relationship was notably more pronounced in newly public firms with a heightened level of firm digitalization. This observation implies that firm digitalization amplifies the impact of transparency on a firm’s idiosyncratic risk.

This study distinguishes itself by providing distinctive insights into supply chain transparency and idiosyncratic risk. Initially, we introduce and substantiate an inverted U-shaped correlation between supply chain transparency and idiosyncratic risk, challenging the conventional linear perspective. Secondly, we pioneer the connection between supply chain transparency and idiosyncratic risk, especially for newly public firms, thereby enhancing comprehension of financial implications. Lastly, we pinpoint crucial digital conditions that influence the relationship between supply chain transparency and idiosyncratic risk management, offering a nuanced perspective on the role of technology in risk management.

The purpose of this paper is to design a climbing robot connected by a connecting rod mechanism to achieve multi-functional tasks such as obstacles crossing and climbing of power transmission towers.

A connecting rod type gripper has been designed to achieve stable grasping of angle steel. Before grasping, use coordination between structures to achieve stable docking and grasping. By using the alternating movements of two claws and the middle climbing mechanism, the climbing and obstacle crossing of the angle steel were achieved.

Through a simple linkage mechanism, a climbing robot has been designed, greatly reducing the overall mass of the robot. It can also carry a load of 1 kg, and the climbing mechanism can perform stable climbing. The maximum step distance of the climbing robot is 543 mm, which can achieve the crossing of angle steel obstacles.

A transmission tower climbing mechanism was proposed by analyzing the working environment. Through the locking ability of the screw nut, stable clamping of the angle steel is achieved, and a pitch mechanism is designed to adjust the posture of the hand claw.

Modular integrated construction (MiC) is a modern construction method innovating and reinventing the traditional site-based construction method. As it integrates advanced manufacturing principles and requires offsite production of volumetric building components, several factors and conditions must converge to make the MiC method suitable and efficient for building projects in each context. This paper aims to present a knowledge-based decision support system (KB-DSS) for assessing a project’s suitability for the MiC method.

The KB-DSS uses 21 significant suitability decision-making factors identified through literature review, consultation of experts and questionnaire surveys. It has a knowledge base, a DSS and a user interface. The knowledge base comprises IF-THEN production rules to compute the MiC suitability score with the efficient use of the powerful reasoning and explanation capabilities of DSS.

The tool receives the inputs of a decision-maker, computes the MiC suitability score for a given project and generates recommendations based on the score. Three real-world projects in Hong Kong are used to demonstrate the applicability of the tool for solving the MiC suitability assessment problem.

This study established the complex and competing significant conditions and factors determining the suitability of the MiC method for construction projects. It developed a unique tool combining the capabilities of expert systems and decision support system to address the complex problem of assessing the suitability of the MiC method for construction projects in a high-density metropolis.

This introductory chapter traces some of the impact COVID-19 has had on education in different global contexts. It traces the history of the Universitas 21 Schools of Education Deans Group and its Forum for International Networking in Education and sets the context for the subsequent contributions in this book. It provides points of reflection for scholars and institutions as they traverse through the ongoing challenges of the pandemic.

The fresh food supply chain industry faces significant challenges in risk management because of the complexity, immature development and unpredictable external environment of imported fresh food supply chains (IFFSCs). This study aims to identify specific risk factors in IFFSCs, demonstrate how these risks are transmitted within the system and provide an analytical framework for managing these risks.

A total of 15 risk factors for IFFSCs through extensive literature review and expert consultation are identified and classified into seven levels using interpretive structural modeling (ISM) to demonstrate the risk transmission path. Fuzzy Matrice d’Impacts Croises-Multiplication Appliance Classement (MICMAC) analysis is then used to analyze the role of each factor.

The interactions of the 15 identified risk factors of IFFSCs, classified into seven levels, are visualized using ISM. The fuzzy MICMAC analysis classifies the factors into four groups, namely, dependent, independent, linkage and autonomous factors, and identifies the relatively critical risk factors in the system.

The findings of this research provide a clear framework for enterprises operating in IFFSCs to understand the specific risks they may face and how these risks interact within the system. The fuzzy MICMAC analysis also classifies and highlights critical risk factors in the system to facilitate the formulation of appropriate mitigation measures.

This study provides enterprises in IFFSCs with a comprehensive understanding of how the risks can be effectively managed and a basis for further exploration. The theoretical model constructed is also a new effort to address the issues of risk in IFFSCs. The ISM and the fuzzy MICMAC analysis offer clear insights for researchers and enterprises to grasp complex concepts.