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In this study, superhydrophobic fabric is prepared with a wet-chemical coating technique that uses a coating solution synthesized by the co-hydrolysis and co-condensation of tetraethyl orthosilicate and fluoroalkyl silane (tridecafluorooctyl triethoxysilane) under an alkaline condition. The treated fabric shows stable superhydrophobicity with a water contact angle as high as 171°, and a sliding angle as low as 2°. The coated fabric has higher repellency to saline water, and its repellency increases with increases in the salt content in the solution. The contact angle is reduced with increases in liquid temperature. When the water temperature is 90°C, the contact angle on the superhydrophobic fabric is 153°. The superhydrophobic treatment slightly reduces the air permeability, but increases the water vapor permeability of the fabric. The treatment considerably increases the liquid breakthrough pressure, but has little effect on fabric pore size and thermal conductivity. The air gap membrane distillation process is used to evaluate the desalination performance of the superhydrophobic fabric. When the feed and the condenser are kept at 90°C and 20°C, respectively, the membrane distillation (MD) system with the superhydrophobic fabric yields a permeate flux of water up to 13.8 kg m^-2 hour^-1, which is slightly higher than that with the use of polymer and inorganic MD membranes reported. Superhydrophobic fabrics may thus be considered as effective MD membranes for water desalination applications.

This paper aims to present a methodology for estimating the risk of component level electrical bridging failures from unattached conductive (tin) whiskers.

Based on experimental data an algorithm was developed and assessed by further experiments. The risk estimate is based on whisker parameters, generated from experiments over a period of time. A bridging failure risk is defined as the probability of a conductive whisker landing between two isolated electrical conductors. A probabilistic estimate for electrical bridging failure risk is achieved by randomly sampling distributions of conductive whisker length, deposition angle, and density for a defined electrical structure. A fine pitch quad flat package attached to a printed wiring board is used as test vehicle to verify the risk estimate.

The estimated risk is found to be higher than planned in the experimental test. The lower experimentally determined risk was found to be the result of high contact resistance between the conductive whisker and the electrical conductors that form the unintended circuit. Contact resistance between the whisker and electrical conductors was found to mitigate the whisker shorting risk.

This is the first attempt to quantify the risk failure due to unattached conductive whiskers in electronic products. A methodology for estimating electrical bridging risk due to unattached conductive whiskers is provided. Contact resistance of conductive whiskers is found to be a critical issue that may be mitigate failure risks.

To present a methodology, including the algorithms, to quantify the risk of failure from tin whiskers and to present a dynamic risk trend based on the distribution of each of the whisker growth parameters, generated from experiments over a period of time. This paper also aims to demonstrate the practical application of the methodology developed.

This paper has been written to provide a methodology to assess tin whisker risk due to fixed whiskers in electronic products. The risk assessment process has been detailed in the paper. To demonstrate the usefulness of the methodology, a tin whisker risk assessment was conducted for a printed circuit board (PCB) in operation.

Based on the experimental tin whisker growth data it is observed that growth rates of mean length and average density decrease with time. Based on the risk assessment, it was estimated that for the common matte tin over copper finish, the failure risk for the circuit card assembly was 4 per cent over 20 years. It was recommended that, for this product, components with bright tin lead finish should not be used. It was also found that the effectiveness of the conformal coating on this PCB is limited by the relative risk of the components on the board.

The paper provides a new methodology to assess fixed tin whisker risk in electronic products. The methodology provides a dynamic risk trend with time because the algorithm incorporates distributional data of whisker growth and the distributional data as a function of time. This type of assessment was lacking in the previous studies.

The purpose of this paper is to address two essential questions: do perceptual differences regarding dependence matter in determining channel performance, and if so, how?

The paper conducted an empirical study of 347 cellular telephone supplier-retailer dyads in China. A questionnaire survey was employed.

The results reveal that a retailer's perceptual difference of dependence exerts a significant effect on its evaluation of supplier performance only. Retailer trust partially mediates the effect of the perceptual differences on supplier performance and retailer performance. Therefore, the particular side of a dyadic relationship that researchers choose to study matters in an unbalanced dependence relationship.

Managers, depending on their side, should pay close attention to perceptual differences and their consequences and deliberately employ different strategies to ensure effective channel management.

Do differences in parties’ perceptions of dependence influence channel performance? If they do, how do these perceived differences exert direct and indirect impacts? By answering these questions, the authors contribute not only to an understanding of the unique nature of dyadic channel relationships but also to methodological notions about whether to study one side in a dyad.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

Introduction: In a world characterised by volatility, uncertainty, complexity, and ambiguity, change is the only constant. Over the years, human resource management (HRM) has evolved from conventional functions of hiring and firing to being a strategic partner in organisations. Similarly, there has been a paradigm shift in the landscape of artificial intelligence (AI) from being a mere searching tool to the design and development of intelligent robots. Over the years, AI has emerged into a collection of powerful technologies re-inventing different functional areas, including HRM. The application of AI in HRM is perceived as an optimistic opportunity since it ought to bring maximum value at minimum cost. AI focuses on building tools that exhibit human-level intelligence and discernment in making decisions.

Purpose: The purpose of this chapter is to draw deeper insights into the relevance of AI in different functional areas of HRM. Integrating AI into HRM functions such as talent acquisition, training and development, performance management, employee engagement, and the like can help leverage efficiency and create an engaging employee experience. In the wake of Industry 4.0, where digitalisation has become imperative, this chapter explores the integration of AI into specific HR functions for a synergistic competitive advantage in companies. The purpose of this chapter is to signify the integration of AI into four vital functions of HRM, namely talent acquisition, training and development, performance management, and employee engagement. The objective is to chart how companies integrate various AI tools in four specific HRM functions to enhance efficiency. Also, the companies willing to implement AI in their HR functions can refer to the case studies used as exemplars in the chapter.

Methodology: This conceptual chapter is based on the secondary sources, which also build upon case studies of different companies that have implemented AI-enabled solutions and integrated them into different HRM functions and processes per needs. This chapter utilises the conceptual framework of both AI and HRM functions to give deeper insight into the challenges and implementation of technology-enabled solutions.

Findings: AI is used in HRM functions to automate repetitive and operational tasks to shift the focus to more strategic aspects. Despite many advantages of AI and machine learning, very few companies are using it, and companies may integrate technology-enabled solutions based on the size and nature of business.

Organizations across industries are increasingly using Artificial Intelligence (AI) systems to support their innovation processes, supply chains, marketing and sales and other business functions. Implementing AI, firms report efficiency gains from automation and enhanced decision-making thanks to more relevant, accurate and timely predictions. By exposing the benefits of digitizing everything, COVID-19 has only accelerated these processes. Recognizing the growing importance of AI and its pervasive impact, this chapter defines the “social value of AI” as the combined value derived from AI adoption by multiple stakeholders of an organization. To this end, we discuss the benefits and costs of AI for a business-to-business (B2B) firm and its internal, external and societal stakeholders. Being mindful of legal and ethical concerns, we expect the social value of AI to increase over time as the barriers for adoption go down, technology costs decrease, and more stakeholders capture the value from AI. We identify the contributions to the social value of AI, by highlighting the benefits of AI for different actors in the organization, business consumers, supply chain partners and society at large. This chapter also offers future research opportunities, as well as practical implications of the AI adoption by a variety of stakeholders.

This paper aims to examine the risk premium for investors in a changing information environment in the Taiwan, New York and London real estate markets from March 2006 to November 2014. This study attempts to quantify behavioral expectations regarding (or motivation for) investment in the Taiwanese real estate in a changing information environment.

This paper uses the rolling generalised autoregressive conditionally heteroskedastic in mean (GARCH-M) methodology which fixes the problem of conventional GARCH-M methodology.

Empirical evidence suggests that the time-varying risk premium changed for the Taiwan real estate market with a new information set. The risk premium changed from 1.305 per cent per month to −7.232 per cent per month. The study also found persistent volatility shocks from March 2006 to November 2014. No such evidence was found for the New York and London real estate markets. Overall, this study finds evidence of a time-varying risk premium, partly explainable by governmental policies and partly unexplainable.

The use of the index of Standard and Poor’s Taiwan Real Estate Investment Trusts to study the Taiwan real estate industry may have aggregation effects in result.

The present study will provide guidance to investors as well as policymakers regarding the Taiwan real estate market.

This study uses the rolling GARCH-M model, which is a first for the Taiwan real estate market.

The aim of this chapter is to explore to what extent artificial intelligence (AI) could be introduced in new product development (NPD) process to support the decision making from the development to the launch of a new idea. Building on qualitative data from multiple cases from alcohol sector, the study reveals that AI could be introduced to reduce the risk of unsuccessful development and launch of a new product, supporting all the phases by systematically integrating feedbacks from the market. Specifically, AI can be used to create new recipes/flavours for alcohol drinks. Results also show that integrating AI from the idea scoping to the final product launch is feasible with the support of external stakeholders. The chapter concludes with some recommendations for theory and practice.

The pressure to survive in a highly competitive market by using artificial intelligence (AI) has further demonstrated the need for automation in business operations during a crisis, such as COVID-19. Prior research finds managers' mixed perceptions about the use of technology in business, which underscores the need to better understand their perceptions of adopting AI for automation in business operations during COVID-19. Based on social exchange theory, the authors investigated managers' perceptions of using AI in business for effective operations during the COVID-19 pandemic.

This study collected data through a survey conducted in China (N = 429) and ran structural equation modeling to examine the proposed research model and structural relationships using Smart PLS software.

The results show that using AI in supply chain management, inventory management, business models, and budgeting are positively associated with managers' satisfaction. Further, the relationship between managers' satisfaction and effective business operations was found to be positively significant. In addition, the findings suggest that top management support and the working environment have moderating effects on the relationship between managers' satisfaction and effective business operations.

The results of this study can guide firms to adopt an AI usage policy and execution strategy, according to managers' perceptions and psychological responses to AI.

The study can be used to manage the behavior of managers within organizations. This will ultimately improve society's perception of the employment of AI in business operations.

The study's outcomes provide valuable insights into business management and information systems with AI application as a business response to any crisis in the future.