The role of artificial intelligence in business transformation: A case of pharmaceutical companies

Artificial intelligence (AI) is of great interest to researchers and practitioners as a means of achieving the necessary progress in the pharmaceutical industry. However, the role of AI and ways of transforming companies are not well studied. The purpose of the paper is to identify exactly how AI affects the key and support business processes of pharmaceutical companies. We offer a qualitative interview study of five large, five medium, and five small pharmaceutical companies. Based on scarce literature on the role of AI in the pharmaceutical industry, we considered which business processes are subject to transform within it and how they do so. We determine that small pharma companies significantly change research and development, master data management, analysis and reporting, and human resource business processes under the influence of AI. Large pharma companies use AI to transform production, sales, marketing, and analysis business processes. In turn, medium-sized companies are in the middle and individually transform their business processes depending on their specialization.     

Novel AR-based interface for human-robot interaction and visualization

Intuitive and efficient interfaces for humanrobot interaction(HRI) have been a challenging issue in robotics as it is essential for the prevalence of robots supporting humans in key areas of activities.This paper presents a novel augmented reality(AR) based interface to facilitate human-virtual robot interaction.A number of human-virtual robot interaction methods have been formulated and implemented with respect to the various types of operations needed in different robotic applications.A EucUdean distance-based method is developed to assist the users in the interaction with the virtual robot and the spatial entities in an AR environment.A monitor-based visualization mode is adopted as it enables the users to perceive the virtual contents associated with different interaction methods,and the virtual content augmented in the real environment is informative and useful to the users during their interaction with the virtual robot.Case researches are presented to demonstrate the successful implementation of the AR-based HRI interface in planning robot pick-and-place operations and path following operations.     

e-Testing from artificial intelligence approach

This paper presents a review of advanced technologies for e-testing using an artificial intelligence approach. First, this paper introduces state-of-the-art uniform test assembly methods to guarantee examinee test score equivalence even if different examinees with the same ability take different tests. More formally, each uniform test form has equivalent measurement accuracy but with a different set of items. To increase the number of assembled tests, some test assembly methods allow that any two tests of uniform tests can include fewer common items than a user allows as a test constraint. This situation is designated as the overlapping condition. However, these methods used with an overlapping condition are often adversely affected by bias of the item exposure frequency and decreased reliability of items and tests. Second, this paper introduces state-of-the-art uniform test form assembly with a constraint of item exposure. Most earlier studies of e-testing employ item response theory (IRT) to obtain each examinee’s test score. However, IRT has several strict assumptions. Recently, Deep-IRT, which employs deep learning to relax the assumptions, has attracted attention. Finally, this paper introduces Deep-IRT models.

     

The impact of automation and artificial intelligence on worker well-being

Discourse surrounding the future of work often treats technological substitution of workers as a cause for concern, but complementarity as a good. However, while automation and artificial intelligence may improve productivity or wages for those who remain employed, they may also have mixed or negative impacts on worker well-being. This study considers five hypothetical channels through which automation may impact worker well-being: influencing worker freedom, sense of meaning, cognitive load, external monitoring, and insecurity. We apply a measure of automation risk to a set of 402 occupations to assess whether automation predicts impacts on worker well-being along the dimensions of job satisfaction, stress, health, and insecurity. Findings based on a 2002–2018 dataset from the General Social Survey reveal that workers facing automation risk appear to experience less stress, but also worse health, and minimal or negative impacts on job satisfaction. These impacts are more concentrated on workers facing the highest levels of automation risk. This article encourages new research directions by revealing important heterogeneous effects of technological complementarity. We recommend that firms, policymakers, and researchers not conceive of technological complementarity as a uniform good, and instead direct more attention to mixed well-being impacts of automation and artificial intelligence on workers.     

Engineering design education: the potential offered by artificial intelligence techniques

Current approaches to the teaching of engineering design are reviewed, as are current approaches to the use of computers in designing and in design education. Recent trends in research into the application of artificial intelligence techniques to education are identified. Some ways in which design education might benefit from intelligent tutoring systems research results (in terms of a growing understanding of teaching techniques and learning processes) are proposed. Some potential advantages of applying those results directly in design education are identified.     

Operator support systems and artificial intelligence

Operator support systems cover a huge area of applications. They are widely used in process plants where Distributed Control Systems (DCS) carry out many of the more basic control functions. Operator support systems are often expected to perform higher-level functions where complex decisions (e.g. in planning, scheduling or fault diagnosis) are based on patterns of symptoms that are changing (perhaps rapidly) in real time. These systems are complex and often costly, yet the potential benefits are large. Some systems would show full pay-back if they prevented the operator from making a single incorrect safety-related decision. Artificial intelligence offers many attractive concepts, techniques and tools that help build complex operator support systems. This paper attempts to review the use of Artificial intelligence from three different perspectives within the industry.     

Investigating the applications of artificial intelligence in cyber security

Scientometrics - Artificial Intelligence (AI) provides instant insights to pierce through the noise of thousands of daily security alerts. The recent literature focuses on AI’s application to...     

The use of artificial intelligence techniques in a catalytic probe modeling

When studying atomic hydrogen interactions with solid surfaces, several problems appear, including the problem of atomic hydrogen concentration measurement. In this work we analyse the use of catalytic nickel probe for the H concentration measurement. We use novel artificial intelligence techniques to obtain differential equations that describe the behavior of the probe directly from measurements. The equations show that the recombination coefficient for the atomic H on the nickel surface does not change substantially with the probe temperature which means that the catalytic nickel probe could be used as a good indicator of the atomic H concentration in the gas.     

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.     

Factors influencing the use of artificial intelligence in government: Evidence from China

Some studies have discussed the potential and challenges related to the use of artificial intelligence (AI) in government. However, there are few empirical studies that have examined factors that influence the use of AI in government. By collecting policy documents and empirical data from the government, IT enterprises, and the public in China, we identified the influencing factors in the three stages of government adoption, implementation, and decision-making. The research results show that the influencing factors of government application of AI are different at different stages and with different stakeholders’ backgrounds.     

Exploring the influence of artificial intelligence technology on consumer repurchase intention: The mediation and moderation approach

Digital technologies have dramatically changed business practices and consumer buying behavior. This study integrates artificial intelligence technology, consumer engagement on social media, conversion rate optimization, and satisfying consumer experience to examine consumer repurchase intentions in the hospitality industry. Data was collected from 308 hotel customers from different regions of Oman who had an online hotel booking experience. SmartPLS was used to examine the data and proposed hypotheses. The findings of this study revealed that artificial intelligence technology positively influences consumer engagement on social media and conversion rate optimization. Similarly, consumer engagement on social media and conversion rate positively influence satisfying consumer experience, leading to increased consumer repurchase intentions. Finally, consumer habit positively moderates the relationship between satisfying consumer experience and repurchase intention. This study facilitated the understanding of artificial intelligence technology to influence consumer engagement on social media and conversion rate to boost consumer satisfaction and repurchase intention and offers suggestions for developing impeccable service business strategies. Marketers must think about making posts more interesting through videos, images, and animations, which will satisfy consumers, ultimately boosting their desire to use, share, and generate content on social media platforms for hospitality organizations.     

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.     

The role of the Si-suboxide structure at the interface: an angle-scanned photoelectron diffraction study

Photoelectron diffraction patterns of the different silicon sub-oxides have been recorded and compared with simulated patterns for various model structures. Each silicon sub-oxide is embedded in an ordered environment since individual diffraction patterns and differences among them were exhibited. In particular, the intensity maxima are located at different angles. In the simulation the silicon-oxide/silicon interface was assumed to be abrupt and within one atomic layer. Excellent agreement between experimental and calculated patterns was achieved. At the interface, horizontally compressed SiO₂ was found. Furthermore, the highest oxidation state of silicon, Si⁴⁺, displays a diffraction pattern indicating an ordered structure for this chemical state.     

Evolutionary stages and multidisciplinary nature of artificial intelligence research

Scientometrics - This paper analyzed the growth and multidisciplinary nature of Artificial Intelligence research during the last 60 years. Web of Science coverage since 1960 was...     

The role of interface energies in galvanized coating development

A new mechanism is advanced that accounts for the abnormal galvanizing behavior of steels containing silicon. The basis for this model is the influence of interface energies on intermetallic phase formation. High silicon and iron concentrations in the liquid adjacent to the developing coating can trigger liquid phase penetration at various interfaces. This results in abrupt changes in the kinetics of the coating development. Observed microstructures conform well to the proposed mechanism.     

Dimensions of artificial intelligence anxiety based on the integrated fear acquisition theory

With the rapid development of artificial intelligence (AI), AI anxiety has emerged and is receiving widespread attention, but research on this topic is not comprehensive. Therefore, we investigated the dimensions of AI anxiety using the theoretical model of integrated fear acquisition and a questionnaire survey. A total of 494 valid questionnaires were recovered. Through a first-order confirmatory factor analysis (CFA), a factor model of AI anxiety was constructed, and eight factors of AI anxiety were verified. Then, a second-order CFA was applied to verify the adaptation of the factor structure of AI anxiety to fear acquisition. We identified four dimensions of AI anxiety and proposed a theory of AI anxiety acquisition that illustrates four pathways of AI anxiety acquisition. Each pathway includes two factors that cause AI anxiety. We conclude by analyzing the limitations of current AI anxiety research and proposing a broader research agenda for AI anxiety.