Impact of artificial intelligence investment on firm value

As artificial intelligence (AI) has recently gained momentum and attention, the interest and investment in AI have also accelerated. However, the impact of AI on firm value is rarely discussed. On the basis of the 119 announcements of 62 listed firms who have invested in AI, this study finds that AI investment has a negative impact on the firms’ market value. The stock prices of the firms decrease by 1.77% on the day of the announcement. Nonmanufacturing firms and firms with weak information technology capabilities or low credit ratings suffer a more negative impact compared with other firms. The findings suggest that investors perceive AI investment announcements to be unwelcome news for the majority of firms. Subsequently, the characteristics affecting the shareholders’ reaction towards AI adoption are presented. This research offers one of the first empirical evidence about the market value of AI and provides a reference for firms interested in investing in AI.

     

Disruptive technologies in agricultural operations: a systematic review of AI-driven AgriTech research

The evolving field of disruptive technologies has recently gained significant interest in various industries, including agriculture. The fourth industrial revolution has reshaped the context of agricultural technology (AgriTech) with applications of artificial intelligence (AI) and a strong focus on data-driven analytical techniques. Motivated by the advances in AgriTech for agrarian operations, the study presents a state-of-the-art review of the research advances which are, evolving in a fast pace over the last decades (due to the disruptive potential of the technological context). Following a systematic literature approach, we develop a categorisation of the various types of AgriTech, as well as the associated AI-driven techniques which form the continuously shifting definition of AgriTech. The contribution primarily draws on the conceptualisation and awareness about AI-driven AgriTech context relevant to the agricultural operations for smart, efficient, and sustainable farming. The study provides a single normative reference for the definition, context and future directions of the field for further research towards the operational context of AgriTech. Our findings indicate that AgriTech research and the disruptive potential of AI in the agricultural sector are still in infancy in Operations Research. Through the systematic review, we also intend to inform a wide range of agricultural stakeholders (farmers, agripreneurs, scholars and practitioners) and to provide research agenda for a growing field with multiple potentialities for the future of the agricultural operations.

     

Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions

In this digital era, data is new oil and artificial intelligence (AI) is new electricity, which is needed in different elements of operations management (OM) such as manufacturing, product development, services and supply chain. This study explores the feasibility of AI utilization within an organization on six factors such as job-fit, complexity, long-term consequences, affect towards use, social factors and facilitating conditions for different elements of OM by mining the collective intelligence of experts on Twitter and through academic literature. The study provides guidelines for managers for AI applications in different components of OM and concludes by presenting the limitations of the study along with future research directions.

     

Evaluating Web Services: Towards a framework for emergent contexts

The paper develops an evaluation framework with specific reference to Web Services. It is argued that the essential characteristics for such an approach, noted as qualitative, are captured in these constructs through an augmentation of theoretical considerations and empirical findings. A review of the innovation and diffusion literature indicates a considerable amount of research where attention is given to a range of features which may support Web Service adoption. It is argued that the framework proposed in this paper is of value in highlighting the specific situations for an effective evaluation in this respect.The empirical analysis was undertaken within five UK firms. Each of these organizations represent different business sectors including, Telecommunication (two firms), Independent Software Package Vendor (ISV), Software Engineering and Food Services industries. The study involved a cross-section organizational case approach within each situation for factors affecting adoption. The evidence suggests that the empirical data complements traditional innovation and diffusion theories. However, emergent context is identified as a significant influence on potential web evaluation processes to ensure appropriate investment targeting. The findings are noted to guide analysts in determining critical aspects of the complex issues involved and present suggestions for further valid research.     

Artificial intelligence for decision support systems in the field of operations research: review and future scope of research

Operations research (OR) has been at the core of decision making since World War II, and today, business interactions on different platforms have changed business dynamics, introducing a high degree of uncertainty. To have a sustainable vision of their business, firms need to have a suitable decision-making process at each stage, including minute details. Our study reviews and investigates the existing research in the field of decision support systems (DSSs) and how artificial intelligence (AI) capabilities have been integrated into OR. The findings of our review show how AI has contributed to decision making in the operations research field. This review presents synergies, differences, and overlaps in AI, DSSs, and OR. Furthermore, a clarification of the literature based on the approaches adopted to develop the DSS is presented along with the underlying theories. The classification has been primarily divided into two categories, i.e. theory building and application-based approaches, along with taxonomies based on the AI, DSS, and OR areas. In this review, past studies were calibrated according to prognostic capability, exploitation of large data sets, number of factors considered, development of learning capability, and validation in the decision-making framework. This paper presents gaps and future research opportunities concerning prediction and learning, decision making and optimization in view of intelligent decision making in today’s era of uncertainty. The theoretical and managerial implications are set forth in the discussion section justifying the research questions.

     

Artificial Intelligence Approaches to Simulation

Recent years have seen a number of attempts to apply both AI methods and AI software tools to the development of both simulations and simulation packages. This paper discusses the motivation behind such work: the increasing need for AI researchers to include simulation in AI systems and use simulation in research, and the need to model intelligent behaviour in OR simulation models. The current research and development effort is reviewed. A system called PROSS (the PROlog Simulation System), constructed to allow the integration of AI methods with traditional discrete simulation, is then briefly presented. It is concluded that AI software tools, and some of the novel approaches to simulation being taken by AI researchers, will have a considerable impact on simulation.     

“You have to get wet to learn how to swim” applied to bridging the gap between research into personnel scheduling and its implementation in practice

Personnel scheduling problems have attracted research interests for several decades. They have been considerably changed over time, accommodating a variety of constraints related to legal and organisation requirements, part-time staff, flexible hours of staff, staff preferences, etc. This led to a myriad of approaches developed for solving personnel scheduling problems including optimisation, meta-heuristics, artificial intelligence, decision-support, and also hybrids of these approaches. However, this still does not imply that this research has a large impact on practice and that state-of-the art models and algorithms are widely in use in organisations. One can find a reasonably large number of software packages that aim to assist in personnel scheduling. A classification of this software based on its purpose will be proposed, accompanied with a discussion about the level of support that this software offers to schedulers. A general conclusion is that the available software, with some exceptions, does not benefit from the wealth of developed models and methods. The remaining of the paper will provide insights into some characteristics of real-world scheduling problems that, in the author’s opinion, have not been given a due attention in the personnel scheduling research community yet and which could contribute to the enhancement of the implementation of research results in practice. Concluding remarks are that in order to bridge the gap that still exists between research into personnel scheduling and practice, we need to engage more with schedulers in practice and also with software developers; one may say we need to get wet if we want to learn how to swim.

     

The impact of the computer on the teaching of analysis

The computer creates new opportunities for analysis instruction. Some traditional motivations for treating conceptually exacting analysis in school can, however, no longer be maintained without further discussion, for instance: calculations such as finding extreme values or areas can be easily programmed without analysis, and practical applications, as in physics or technology, use discrete methods in computer programs.

This results in a crisis: the legitimacy of traditional analysis in school is challenged. This challenge is analysed, consequences for goals and methods of analysis teaching at schools are named, and some initial examples are given, as to how this could be realized with the help of the computer.     

Representing context in video records of practice for urban mathematics teacher education

Mathematics education research has not sufficiently theorized about mathematics teacher knowledge and practice, teacher learning, and teacher education in ways that are reflective of the specificities of the sociopolitical contexts of schooling. In the USA, this is particularly important for urban mathematics education. This paper examines the affordances and challenges of representing context in video records of practice, particularly in the urban context, for use in the preparation of mathematics teachers for urban settings. The discussion, grounded in current research and theory relevant to representations of teaching, urban education, and mathematics teacher education, takes up three key issues: how is a focus on the urban context relevant to the design of video records of practice for mathematics teacher education? How can video records support prospective teachers’ understandings of the sociopolitical contexts of mathematics teaching? How does a focus on the urban context impact the meaning teachers make of video records?     

Investigating the Role of Context in Experimental Research Involving the Use of Digital Media for the Learning of Mathematics: Boundary Objects as Vehicles for Integration

The paper describes a study of the contexts of six teams, expert in research and development of digital media for learning mathematics, who cross-experimented in classrooms with the use of each other’s artefacts. Contextual issues regarding the designed tasks and technologies, the socio-systemic milieu and the ways in which the researchers worked with the teachers were in focus. We analysed the ways in which a set of mutually constructed and negotiated questions aiming to illuminate otherwise tacit contextual issues operated as boundary objects amongst the teams. We discuss the need to develop special tools such as these boundary objects in order to elicit issues of context and the ways they may affect the production of theory.     

Adapting RRI public engagement indicators to the Spanish scientific and innovation context: a participatory methodology based on AHP and content analysis

The paradigm proposed by Responsible Research and Innovation in the European Commission policy discourse identifies Public Engagement as a key area for exchange and dialogue among multiple actors following an inclusive and participatory process. Two definite set of indicators have already arisen at European level to monitor Public Engagement activities in the Science and Innovation realm. Our study aims to propose a deliberative participatory process, which involves selected stakeholders, for the adaptation of the European indicators to the specific Spanish scientific and innovation context. The methodological procedure is of exploratory nature and will be based in a combination of, on the one hand, qualitative content analysis techniques for the in-depth study of the deliberative process and the generation of indicators; and, on the other hand, a multi-criteria decision analysis technique such as the Analytic Hierarchy Process for the prioritization of the indicators. The discussion will focus on the procedure to articulate stakeholders’ values and use them as the basis for creating a context-based improved list of indicators. Two types of research questions arise: (i) Is the proposed methodology adequate for the adaptation of the European indicators to the Spanish context? (ii) What are the main indicators to monitor and to expand reflection on the public engagement in the Spanish science and innovation?

     

On the use of computational tools to promote students’ mathematical thinking

This column will publish short (from just a few paragraphs to ten or so pages), lively and intriguing computer-related mathematics vignettes. These vignettes or snapshots should illustrate ways in which computer environments have transformed the practice of mathematics or mathematics pedagogy. They could also include puzzles or brain-teasers involving the use of computers or computational theory. Snapshots are subject to peer review. In this snapshot students employ dynamic geometry software to find great mathematical richness around a seemingly simple question about rectangles.

Editor: Uri Wilensky

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Mathematical tables in Ptolemy's Almagest

This paper is a discussion of Ptolemy's use of mathematical tables in the Almagest. By focusing on Ptolemy's mathematical practice and terminology, I argue that Ptolemy used tables as part of an organized group of units of text, which I call the table nexus. In the context of this deductive structure, tables function in the Almagest in much the same way as theorems in a canonical work, such as the Elements, both as means of presenting acquired knowledge and as tools for producing further knowledge.     

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested.     

A Discussion on Variational Analysis in Derivative-Free Optimization

Variational Analysis studies mathematical objects under small variations. With regards to optimization, these objects are typified by representations of first-order or second-order information (gradients, subgradients, Hessians, etc). On the other hand, Derivative-Free Optimization studies algorithms for continuous optimization that do not use first-order information. As such, researchers might conclude that Variational Analysis plays a limited role in Derivative-Free Optimization research. In this paper we argue the contrary by showing that many successful DFO algorithms rely heavily on tools and results from Variational Analysis.

     

Justifying the methods of OR

Operational research practitioners use mathematical, statistical, scientific, and other methods to structure and analyse issues in order to advise and assist their clients. In doing so they apply values, follow rules and use methodologies. The paper examines the justification of these methods, values and methodologies. Starting with a conceptual model drawn from the philosophy of science, a justification framework is developed for operational research (OR). Making a distinction between OR academic research and OR practice helps to clarify the issues. OR research is similar to scientific, mathematical and social science research; OR practice, as technology, is closer to engineering. While OR academic researchers will seek justification in the academic discipline within which they choose to work, it is argued that the justification of OR practice lies in its usefulness. For academic OR, justification lies in the justification of mathematics, statistics, science and social science; for practice, it is practitioners who decide what usefulness means in their context.     

Measurement in the workplace: the case of process improvement in manufacturing industry

In the context of education, measurement is often defined as the process of assigning a numerical value to an attribute of an object or event. Using three case studies of ??process improvement??, the purpose of this article is to show how measurement takes place in manufacturing industry. We ask: What are the key issues involved in measurement that should inform the design of education and training for measurement? First, our research suggests that the definition of measurement should be enhanced so as to include the quantification of processes and multivariate constructs that aim to capture key performance indicators of production processes. Secondly, technology plays a complex role because it can lead not only to automation and increased invisibility of data, but also to the availability of information otherwise not accessible. Thirdly, we suggest that the use of the inferentialist concept of ??web of reasons?? in addition to more commonly used concepts such as ??practice?? or ??activity?? can help to focus not only on the what and how, but also on the why of measurement.     

Hybrid Dynamics of Stochastic π-Calculus

In this paper we describe a method to map stochastic π-calculus processes in chemical ground form into hybrid automata. Hybrid automata are tools widely employed to model systems characterized by both discrete and continuous evolution and their use in the context of Systems Biology allows us to address rather fundamental issues. Specifically, the key ingredient we use in this work is the possibility granted by hybrid automata to implement a separation of control and molecular terms in biochemical systems. The computational counterpart of our analysis turns out to be related to the determination of conservation properties of the system.      

A fifth generation numerical modelling system in coastal zone

Nowadays, artificial intelligence (AI) technology is gradually integrated into the numerical modelling system to make the system more intelligent and more user-friendly. The characteristics of the fifth generation numerical modelling are connected with AI applications. The expert system technology as a widely applied AI technology is integrated into our modelling system for coastal water processes with traditional numerical computational tools and the data and graphical pre-processing and post-processing techniques. Five kinds of knowledge bases are built in the system to describe the existing expertise knowledge about model parameters, relations between parameters and physical conditions, various possible selections for parameters and rules of inference. The inference engine is designed to be driven by the confidence of correctness, and the rule base is built with the factor of confidence to link the various relations. The decision tree is designed to drive the inference engine to explore the route of selection procedure of modeling. The decision tree depends on the real problem specifications and can be modified during the dialogue between the system and the user. The forward chaining and backward chaining inference techniques are mixed together in the system to help matching the parameters in the model and the possible selections with sufficiently high confidence. The expert system technology is successfully integrated into the system to provide help for model parameter selection or model selection, and to make the numerical model system more accessible for non-expert users.