Search results

Introduction: AI technologies are transforming the industrial sectors, and the impact of AI technologies does not leave behind human resource management (HRM). From recruitment to development and payroll, AI has its own impact. In recruitment, selection, training and development, compensation, and remuneration AI play an important role.

Purpose: The main purpose of this chapter is to analyse the challenges in adopting AI in HRM. This chapter investigates the various strategies to overcome challenges encountered by companies while adopting AI in HR practices. Moreover, this chapter also examines the role of AI in HR practices.

Methodology: To achieve the purpose of this chapter, various case studies were analysed and literature studies with emphasis on what types of challenges are face in adopting AI in HR practices. The authors of various newspaper articles, books, published journals, and websites on AI analyse the various newspaper articles, books, published journals, and websites on AI in human resources (HR). Electronic databases are used as the most effective technique to begin a literature search for the current overview, specifically Science Direct, Google Scholar, and Emerald. In addition to this, the application of AI in HRM practices used by leading organisations globally is also reviewed. The analysis used four keywords: HRM, AI, challenges and adopting. This research has been conducted by searching scholarly papers and relevant studies using similar keywords.

Findings: The adoption of AI technologies is continuously increasing in HR practices. AI performs various HR functions such as recruitment, selection, training and development practices, scanning resume, etc. Organisations can be benefitted in many ways by adopting AI in HRM practices, such as better employees engagement and relations, accelerating competitive advantages and effectively utilising HR budgeting. The findings show that many companies like IBM, Deloitte, Amazon, etc., adopt AI in their HR practices.

The new campus of Tianjin University was designed, built and now operates following a green and sustainable concept. The campus’ eco-friendly water environment was formed by establishing a water recycling system. The campus is divided into three drainage sections based on the masterplan. Each drainage section adopts different methods of collecting, utilizing and discharging water according to specific conditions, aimed at achieving both high drainage capability and the efficient utilisation of rainwater. The campus was designed so runoff pollution is reduced through the utilisation of low-impact development methods, ensuring the quality of the recharge water. Through studying the fundamentals of treatment measures and models for simulating water quality, water circulation, constructed wetlands and pollution control of rain runoff, parameters for efficient water recycling could be mathematically forecast, ensuring that stakeholders can be continuously engaged in improving and preserving the water quality of landscaped water on campus. The overall system integrates a variety of measures being implemented into one cohesive entity, which contributes to establishing the sustainable and healthy water cycling system of the green campus.

This chapter describes the complementary benefits of model-building and data analysis using algorithm and statistical modeling methods in the context of unobtrusive marketing field experiments and in transforming findings into isomorphic-management models. Relevant for marketing performance measurement, case-based configural analysis is a relatively new paradigm in crafting and testing theory. Statistical testing of hypotheses to learn net effects of individual terms in MRA equations is the current dominant logic. Isomorphic modeling might best communicate what executives should decide using the findings from algorithm and statistical models. Data testing these propositions here uses data from an unobtrusive field experiment in a retailing context and includes two levels of expertise, four price points, and presence versus absence of a friend (“pal” condition) during the customer-salesperson interactions (n = 240 store customers). The analyses support the conclusion that all three approaches to modeling provide useful complementary information substantially above the use of one or the other alone and that transforming findings from such models into isomorphic-management models is possible.

Internet + and Electronic Business in China is a comprehensive resource that provides insight and analysis into E-commerce in China and how it has revolutionized and continues to revolutionize business and society. Split into four distinct sections, the book first lays out the theoretical foundations and fundamental concepts of E-Business before moving on to look at internet+ innovation models and their applications in different industries such as agriculture, finance and commerce. The book then provides a comprehensive analysis of E-business platforms and their applications in China before finishing with four comprehensive case studies of major E-business projects, providing readers with successful examples of implementing E-Business entrepreneurship projects.

Internet + and Electronic Business in China is a comprehensive resource that provides insights and analysis into how E-commerce has revolutionized and continues to revolutionize business and society in China.

The main objectives of this study were to examine the Lao People’s Democratic Republic (PDR) agricultural exports to the People’s Republic of China (PRC), the tuning of the Agricultural Commodity Frequency Index (ACFI) to non-tariff measures (NTMs), and the coverage ratio of goods to determine the effects of the PRC’s NTMs on Lao PDR’s agricultural exports using a demand export model with a fixed-effect method. The authors found that Lao PDR’s agricultural exports to the PRC increased by an average of 46.91% from 2013 to 2020, covering a total of 51 product codes, comprising six of the most valuable product types (i.e., bananas, corn, tapioca flour, watermelon, sticky rice, and sweet potato) given priority by the PRC. Additionally, from 2013 to 2020, the average ACFI concentration with NTMs was 10.08%, and the average coverage ratio for goods was 14.43%. The results of statistical significance testing at 1% suggest that three factors demonstrated the most significant impact on value: agricultural products facing NTMs in the form of sanitary and phytosanitary (SPS) measures and technical barriers to trade (TBTs), treaties with priority conditions regarding SPSs and priorities for agricultural products, and the real gross domestic product (GDP) of the PRC. Furthermore, a PRC GDP increase of 1% resulted in a 3.1235% impact on Lao PDR exports.

Special education in China has lagged behind regular education for many years, however, the past few decades, the government has made considerable efforts to develop and improve the special education system. While the citizens of China have had a generic moral interest in disability since ancient times, the development of special education schools did not occur until American and European missionaries started schools for the visually and hearing impaired in the 19th century. The next major influence in the development of the special education system occurred with China’s Cultural Revolution in 1978. Interestingly, there is not any exclusive legislation on special education but in the 1980s, the government started Learning in Regular Classrooms (LRC), which is China’s version of inclusion. LRC has progressed rapidly the past two decades; however, the quality of instruction is low due to a lack of specialists, a shortage of personnel, inadequate funding, and limited technology as well as other barriers that are delineated in the chapter. The chapter emphasizes the government’s recent efforts in in-service teacher training, the preparation of preservice teachers, working with families, developing community rehabilitation training programs, and implementing evidence-based practices. Special education in China today is at a good place but it has quite a way from the ideal situation.

Ranking groups (schools, regions, counties) according to the average score of their constituent parts – say, ranking schools by the academic achievements of students – is a common yardstick in evaluation and a cornerstone of any planning process. In this paper we show that under certain circumstances the ranking of groups, unlike the ranking of individuals, can be affected by the examiner. The fact that a ranking reversal is possible does not necessarily imply that the test itself is deficient, it merely reveals that the groups are non-homogeneous and therefore, the ranking of groups is meaningless. An investigation of the conditions under which such manipulation can occur leads us to suggest new statistical indicators as warning signals that can help one recognize such situations if and when they arise. It turns out that these indicators are related to the indicators for finding whether one distribution dominates another.