A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application

More and more products in everyday life are using artificial intelligence (AI). The purpose of this research is to investigate influence factors in an acceptance model on behavioral intention and use behavior for products containing AI in an everyday life environment. Using PLS-Analysis, this study analyzes additional influence factors to the UTAUT2 model in the three application segments mobility, household, and health, using a sample of 21,841 respondents. Except for safety security, all additional factors to the UTAUT2 model play a relevant role in explaining behavioral intention and use behavior of products containing AI. This study answers the applicability of an established acceptance model for products that incorporate AI, extended by five additional influencing factors.     

人工智能转译传统美术图案的公众情感认知解构

目的 人工智能的发展逐渐渗透到人文艺术领域,其中智能辅助艺术创作引领了学科领域发展新趋势,人工智能(AI)与传统美术图案的结合为传统美术的创新与传承带来了新的活力。研究对公众如何认知经由人工智能转译后的传统美术图案及影响此类认知的主要因素。方法 研究运用人工智能图像转译技术,将6幅中国传统艺术图案进行风格迁移和重构,并以网上问卷调查的方式,收集355名受试者对原图案和转译图案的情感评价与审美理解,进而对公众的认知结构进行解构与分析。讨论 研究发现,公众对经由人工智能转译后的传统美术图案的风格契合度的情感认知路径是多元复杂的,其涉及美感、色彩、愉悦、工艺、构图等多重元素的因素组合。结论 公众对人工智能转移后的美术图案的情感认知,除了现有研究所侧重的色彩与纹理等因素,生成图案的整体感以及其展现的情绪色彩也能够对公众契合度认知产生核心影响。     

标准联盟战略动机及影响因素研究

通过标准联盟创立技术标准正成为国际标准化领域的重要趋势,基于文献,本文主要研究企业标准联盟的战略动机及其影响因素.研究表明,企业标准联盟战略动机主要包括降低市场的不确定性、知识的获取、市场准入和与政府政策的一致性,但与一般的战略联盟有所区别;企业R&D强度、市场创新、公司规模、国际贸易、是否设置独立标准化组织以及竞争环境影响着企业标准联盟的战略动机.研究结果具有一定的指导意义.     

Topical review of artificial intelligence national policies: A mixed method analysis

A number of countries have adopted national policies and directives to balance the advantages and disadvantages of innovative technologies. The purpose of this paper is to identify the most prominent topics addressed by national AI policies, as well as their relative importance across nations. This paper integrates the results of a topic modeling analysis of 30 national AI policies with a qualitative content analysis of the policies. Based on this analysis, fourteen main common themes have been identified among national AI policies, which predominantly relate to educational, technological, government, ethical/legal, and social good concerns. Following this, we conducted a co-occurrence analysis of topics across countries to determine the extent of topic prioritization in each country. In this investigation, several marginalized AI policy topics were also identified. In general, the challenges and concerns of the majority of policies pertain to education, technology, and the government. Governments refer to real-world projects and investments in AI technologies without developing shared digital governance platforms that promote responsible and sustainable AI among technology titans and mitigate the negative effects of surveillance capitalism. Although governments acknowledge the ethical and legal aspects of AI development and frequently cite the GDPR, they limit their discussion to the data level, particularly data sharing, and marginalize ethical algorithms and other phases of data and AI management and design. In addition, government policies marginalize AI startups and the API economy, even though they play a crucial role in fostering the AI ecosystem. The paper contributes to the existing literature on AI policy and will serve as a guide for AI policymakers to help them better understand the topical similarities across countries and the neglected or marginalized challenges that require further attention.     

Internationalizing AI: evolution and impact of distance factors

Scientometrics - International collaboration has become imperative in the field of AI. However, few studies exist concerning how distance factors have affected the international collaboration in AI...     

Mapping the barriers of AI implementations in the public distribution system: The Indian experience

With increasing digitization, private organizations have started adopting modern technology to improve the effectiveness and transparency of their system. In countries like India, where most public services are under government control, technology adoption is nascent due to various obstacles. The study considered artificial intelligence (AI) the most popular technology and identified 18 critical adoption barriers in India's public distribution systems (PDS). The study is further extended to find the contextual relationship among barriers using interpretative structural modeling (ISM) and prioritize them using the analytical network process (ANP) method. The study identifies lack of trust in technology, lack of AI literacy, and political issues as significant barriers to AI adoption in PDS. The hybrid methodology used in this study proposed five different strategies for effective and smooth implementation of AI in PDS, which would help the policymaker plan the same.     

Mixed methods in international business research: A value-added perspective

The paper considers the use of mixed methods in international business research by reviewing empirical studies in four major journals. A total of 484 articles were reviewed, 68 of which had applied a mixed method strategy. In our analysis we categorize mixed methods according to their use in different stages of the research process and provide a range of alternative designs for mixing qualitative and quantitative methods.     

Identifying the driving factors of successful megaproject construction management: Findings from three Chinese cases

The construction of megaprojects has always resulted in extensive and long-term impacts on the society. However, the performance of megaproject management is poor, and improving it remains an urgent and necessary issue. Although many studies on megaproject success have been conducted, existing studies on the driving factors of successful megaproject construction are rather limited. Therefore, this study aims to systematically explore the key factors that can lead to successful megaproject construction management based on three cases: The Beijing–Shanghai High-Speed Railway, the Three Gorges Dam, and the Hong Kong–Zhuhai–Macao Bridge. Mixed research methods, such as literature review, case studies, and expert interviews, were used in this study. Consequently, 11 driving factors, namely, government support, public support, accumulation and application of technology and experience, development and innovation of technology, innovation and application of management system, organizational mode and structure, top management support, project culture, megaproject citizenship behavior, corporate reputation, and fulfillment of social responsibilities, were identified and grouped into five categories, namely, project environment, construction capabilities, organization, positive culture and behavior, and requirements for sustainable development. The contributions of this study lie in two aspects. First, the driving factors of successful megaproject construction are identified to deepen the understanding of industrial practitioners, assist them in focusing on key factors, and aid them in effectively managing megaprojects. Second, researchers could use the identified driving factors in conducting further empirical studies and apply them in future projects to enhance their chances of success.     

Management practices of Australian manufacturing firms: why are some firms more innovative?

Whilst many studies have focused on the adoption of individual or sets of innovative management practices (e.g. lean production), fewer studies have evaluated a diverse set of management practices and firm contextual factors which may limit (or enable) the accumulation of groups of innovations in organisations. The Australian manufacturing sector is a novel setting to investigate such issues due to, among other reasons, a protracted decline of the competitive position of the sector. In this paper, we use a data-set from the Australian government funded management practices benchmarking project which was part of the World Management Survey and empirically evaluate why some companies have more innovative management practices than others. The conceptual model developed draws mainly on innovation diffusion theory and prior empirical findings. We find that (1) firms which adopt clusters of better management practices have greater performance; and (2) several firm characteristics explain the adoption of better management practices, such as education level of employees and managers, firm size, ownership by a multinational firm, and diffused ownership structure. The study has practical implications for policy-makers and stakeholders who are interested in supporting the adoption of better management practices by firms to enhance productivity in the manufacturing sector.     

Determinants of mobile supply chain management system diffusion: a structural equation analysis of manufacturing firms

Mobile supply chain management (SCM) is gaining recognition as a major source of cost reduction and supply chain performance improvement. The current literature related to mobile SCM needs to be extended further in order to provide insights into how manufacturing firms can implement mobile SCM successfully. Specifically, there is a need to provide empirical and systematic analysis of the variables that can explain the various stages of mobile SCM diffusion. A review of recent literature suggests that existing e-supply chain technology adoption literature is not strongly grounded in theory. A theoretical model with six hypotheses was proposed based on the technology–organisation–environment (TOE) framework and innovation diffusion theory (IDT). This study draws its survey responses from a group of manufacturing firms in order to investigate the factors that affect the diffusion of mobile SCM. The results show that the variables derived from TOE and IDT can explain mobile SCM diffusion well. However, interorganisational relationships (IORs) play a crucial role in determining the success of mobile SCM routinisation. This is one of the first known empirical studies on the factors influencing the diffusion of mobile SCM. The results of this study will help decision makers better understand the implementation process of mobile SCM and formulate strategies for successful diffusion of mobile SCM.     

The effect of digitalized workplace on employees' psychological well-being: Digital Taylorism approach

The aim of the study is to investigate the effects of emotional labor and artificial intelligence anxiety on the psychological well-being of employees in call centers in the context of Digital Taylorism principles. In this context, the research was structured as two studies. Study-1 presents evidence showing that call centers are fields of study that effectively implement Digital Taylorism principles and provide a suitable environment for research on this subject. Study-1. Using the qualitative research method in Study-1, semi-structured questions were asked to 14 middle and senior managers working in call centers and the results were reported. In Study-2, the moderator role of autonomy in the effect of emotional labor on psychological well-being and the effect of AI anxiety on psychological well-being was investigated. The data obtained through the survey applied to 606 employees working in one of the largest call centers in Turkey were analyzed. The findings revealed that emotional labor dimensions have different effects on psychological well-being and that autonomy has different moderator effects on these relationships. Moreover, the findings revealed that autonomy has a moderation effect on the relationship between two the dimensions of emotional labor. In addition, while it was shown that learning anxiety had a significant effect on psychological well-being, job-replacement anxiety did not have a significant effect. The research is one of the rare studies that provide empirical evidence that the call centers are managing with Taylor's principles by using digital technologies. In addition, the research is one of the pioneering studies that provide empirical evidence about the effect of Digital Taylorism on employees.     

Drivers,barriers and social considerations for AI adoption in business and management: A tertiary study

The number of academic papers in the area of Artificial Intelligence (AI) and its applications across business and management domains has risen significantly in the last decade, and that rise has been followed by an increase in the number of systematic literature reviews.The aim of this study is to provide an overview of existing systematic reviews in this growing area of research and to synthesise the findings related to drivers, barriers and social implications of the AI adoption in business and management.The methodology used for this tertiary study is based on Kitchenham and Charter's guidelines [14], resulting in a selection of 30 reviews published between 2005 and 2019 which are reporting results of 2021 primary studies.These reviews cover the AI adoption across various business sectors (healthcare, information technology, energy, agriculture, apparel industry, engineering, smart cities, tourism and transport), management and business functions (HR, customer services, supply chain, health and safety, project management, decision-support, systems management and technology adoption).While the drivers for the AI adoption in these areas are mainly economic, the barriers are related to the technical aspects (e.g. availability of data, reusability of models) as well as the social considerations such as, increased dependence on non-humans, job security, lack of knowledge, safety, trust and lack of multiple stakeholders’perspectives.Very few reviews outside of the healthcare management domain consider human, organisational and wider societal factors of the AI adoption.In addition to increased focus on social implications of AI, the reviews are recommending more rigorous evaluation, increased use of hybrid solutions (AI and non-AI) and multidisciplinary approach to AI design and evaluation.Furthermore, this study found that there is a lack of systematic reviews in some of the early AI adoption sectors such as financial industry and retail.     

水凝胶载体应用于药物控释的数学建模进展

聚合物水凝胶是由亲水性聚合物链通过物理作用或化学键作用形成的三维网络结构,在受到环境变化刺激时会产生响应性,因而作为药物控释载体广泛应用于医药领域。文中介绍了可控释药系统的类型及其主要影响因素,综述了可控释药数学模型的发展与应用,比较性分析了经验/半经验模型与机理模型,并展望了未来的发展方向。     

Lessons learned from an unsuccessful “catching-up” in the automobile industry of Iran

In contrast to existing studies mostly investigating successful cases of technological catching-up, this paper aims to analyse a limited catch-up by an Iranian automobile firm. Using thematic analysis, and based on the conceptual framework developed in the literature review, the empirical data were coded and categorised into the themes and dimensions. The resulting contextualized framework, as one of the main contributions of this study, suggests how the certain sectoral environment of the automobile industry contributes to the lackluster performance of catching-up by the domestic firm. The sectoral environment (i.e. technological regimes, market regimes, and government policies) of Iran's automobile industry (IAI) is characterized by ineffective technology transfer, diverse technological areas, high tacit knowledge, the extensive backward linkages, high scale intensity, under-developed domestic supply chain, capital-goods attitude to the consumer goods, the oligopolistic structure, and homogeneous market. This paper also identifies new catch-up's influencing factors (i.e. industrial strategy and structural issues) inductively emerged during the empirical data analysis. And finally, comparison of the automobile industry in Iran and a few Asian countries shows that local content requirements (LCRs) will not bring about indigenous development unless it is complemented by the export market discipline and scale economy.     

A note on modeling vehicle accident frequencies with random-parameters count models

In recent years there have been numerous studies that have sought to understand the factors that determine the frequency of accidents on roadway segments over some period of time, using count data models and their variants (negative binomial and zero-inflated models). This study seeks to explore the use of random-parameters count models as another methodological alternative in analyzing accident frequencies. The empirical results show that random-parameters count models have the potential to provide a fuller understanding of the factors determining accident frequencies.     

Collective action on artificial intelligence: A primer and review

Progress on artificial intelligence (AI) requires collective action: the actions of two or more individuals or agents that in some way combine to achieve a result. Collective action is needed to increase the capabilities of AI systems and to make their impacts safer and more beneficial for the world. In recent years, a sizable but disparate literature has taken interest in AI collective action, though this literature is generally poorly grounded in the broader social science study of collective action. This paper presents a primer on fundamental concepts of collective action as they pertain to AI and a review of the AI collective action literature. The paper emphasizes (a) different types of collective action situations, such as when acting in the collective interest is or is not in individuals’ self-interest, (b) AI race scenarios, including near-term corporate and military competition and long-term races to develop advanced AI, and (c) solutions to collective action problems, including government regulations, private markets, and community self-organizing. The paper serves to bring an interdisciplinary readership up to speed on the important topic of AI collective action.     

金属材料表面抗空蚀涂层的研究进展

概括了金属材料空蚀机理及其影响因素,综合评述了采用激光表面改性技术、等离子表面改性技术、热喷涂技术、化学镀、电镀等表面涂镀技术制备的不同种类涂层的抗空蚀作用效果与机理,提出并讨论了目前研究中存在的科学问题及未来抗空蚀涂层的研究方向。     

Evaluation of postponement strategies in mass customization with service guarantees

Postponement is one of the practical methods to mass customization. Product redesign approach and process reversal approach are two strategies applied to delay the point of differentiation in mass customization systems. In the existing literature, analytical models explaining and quantifying the benefits of delayed differentiation have been developed. These models focus on analysing the costs and benefits of certain postponement strategies. In this paper, we analyze and compare two different postponement strategies applied in a mass customization system with service time guarantees. The goal is to determine the right strategy and to identify the key influencing factors. We formulate a cost-minimisation model with general distributed demand. We show that it is not beneficial to delay the point of differentiation when the considered stage is a high value-added process. We also show that processing time and value added at the considered stages are two main factors determining the choice of the right strategy.