An integrated artificial intelligence framework for knowledge production and B2B marketing rational analysis for enhancing business performance

2021 ◽  
Author(s):  
Abhishek Dondapati ◽  
Neelam Sheoliha ◽  
Jeidy Panduro-Ramirez ◽  
Ruhi Bakhare ◽  
P.M. Sreejith ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Production ◽  
Business Performance ◽  
Rational Analysis ◽  
B2b Marketing

scholarly journals Unsupervised by any other name: Hidden layers of knowledge production in artificial intelligence on social media

2019 ◽  
Vol 6 (1) ◽  
pp. 205395171881956 ◽  
Author(s):  
Anja Bechmann ◽  
Geoffrey C Bowker
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Knowledge Production ◽  
Classification Theory ◽  
Human Knowledge ◽  
Learning Models ◽  
Work Process ◽  
Learning Development ◽  
High Degree ◽  
Machine Learning Models

Artificial Intelligence (AI) in the form of different machine learning models is applied to Big Data as a way to turn data into valuable knowledge. The rhetoric is that ensuing predictions work well—with a high degree of autonomy and automation. We argue that we need to analyze the process of applying machine learning in depth and highlight at what point human knowledge production takes place in seemingly autonomous work. This article reintroduces classification theory as an important framework for understanding such seemingly invisible knowledge production in the machine learning development and design processes. We suggest a framework for studying such classification closely tied to different steps in the work process and exemplify the framework on two experiments with machine learning applied to Facebook data from one of our labs. By doing so we demonstrate ways in which classification and potential discrimination take place in even seemingly unsupervised and autonomous models. Moving away from concepts of non-supervision and autonomy enable us to understand the underlying classificatory dispositifs in the work process and that this form of analysis constitutes a first step towards governance of artificial intelligence.

Artificial Intelligence in the Management of Knowledge Production, Organizations, and Teams

2019 ◽  
Vol 2019 (1) ◽  
pp. 15505
Author(s):  
Ella Glikson ◽  
Pranav Gupta ◽  
Anita Williams Woolley ◽  
Paul Leonardi ◽  
Samer Faraj ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Production

AI risks being misunderstood in the workplace

2019 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Business Performance ◽  
Use Cases ◽  
Positive Outcomes ◽  
Negative Outcomes ◽  
Content Type ◽  
Effective Use

Subject AI in the workplace. Significance Positive use cases for artificial intelligence (AI) systems are rising, but misuse means the number of negative examples is also rising, drawing attention to how to regulate it. Impacts Effective use of AI within appropriate contexts will improve business performance in many sectors. Current law is not suitable for some emerging forms of AI, but to gain competitiveness, some regions may prioritise efficiency over safety. Misuse of AI will become a major source of negative outcomes at work, likely outweighing the positive outcomes. Future uses of AI will become increasingly hard to manage or regulate. Firms expanding their 'ethical' activities and then arguing that more regulation would limit them will raise fears of ‘ethical washing’.

scholarly journals Artificial intelligence in business-to-business marketing: a bibliometric analysis of current research status, development and future directions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Runyue Han ◽  
Hugo K.S. Lam ◽  
Yuanzhu Zhan ◽  
Yichuan Wang ◽  
Yogesh K. Dwivedi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Large Scale ◽  
Relevant Literature ◽  
Future Research ◽  
Specific Information ◽  
Future Directions ◽  
Content Type ◽  
B2b Marketing ◽  
Marketing Innovation ◽  
Business To Business

PurposeAlthough the value of artificial intelligence (AI) has been acknowledged by companies, the literature shows challenges concerning AI-enabled business-to-business (B2B) marketing innovation, as well as the diversity of roles AI can play in this regard. Accordingly, this study investigates the approaches that AI can be used for enabling B2B marketing innovation.Design/methodology/approachApplying a bibliometric research method, this study systematically investigates the literature regarding AI-enabled B2B marketing. It synthesises state-of-the-art knowledge from 221 journal articles published between 1990 and 2021.FindingsApart from offering specific information regarding the most influential authors and most frequently cited articles, the study further categorises the use of AI for innovation in B2B marketing into five domains, identifying the main trends in the literature and suggesting directions for future research.Practical implicationsThrough the five identified domains, practitioners can assess their current use of AI and identify their future needs in the relevant domains in order to make appropriate decisions on how to invest in AI. Thus, the research enables companies to realise their digital marketing innovation strategies through AI.Originality/valueThe research represents one of the first large-scale reviews of relevant literature on AI in B2B marketing by (1) obtaining and comparing the most influential works based on a series of analyses; (2) identifying five domains of research into how AI can be used for facilitating B2B marketing innovation and (3) classifying relevant articles into five different time periods in order to identify both past trends and future directions in this specific field.

scholarly journals Empowering IT Operations through Artificial Intelligence – A New Business Perspective

2020 ◽  
Author(s):  
Logica Banica ◽  
Persefoni Polychronidou ◽  
Cristian Stefan ◽  
Alina Hagiu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Business Performance ◽  
Business Environment ◽  
Infrastructure Monitoring ◽  
Parallel Data ◽  
New Business ◽  
Main Components ◽  
It Operations

This paper aims to describe the concept of applying Artificial Intelligence to IT Operations (AIOps) and its main components, Big Data, Machine Learning and Trend Analysis. The concept was implemented by developing a multi-layered fusion of the technologies that powers the components in AIOps platforms present on the IT market. The core of an AIOps platform is represented by the Big Data organization structure and by a massive parallel data processing platform like Apache Hadoop. The ML component of the platform is able to infer the future behaviour and the regular operations that are performed from the large volume of collected data, in order to develop the ability to automate the activities. AIOps platforms find their place especially in very complex IT infrastructures, ones that require constant monitoring and quick decisions in case of failures. The case study is based on the Moogsoft AIOps platform, and its features are presented in detail, using the Cloud trial version, clearly showing the potential of such an advanced tool for infrastructure monitoring and reporting. The experiment was focused on the way Moogsoft is monitoring computing resources,    is handling events and records alerts for the defined timespan, alerts grouped by category (like web services, social media, networking). The platform is also able to display at any given moment the unresolved situations and their type of origin, and includes automated remediation tools. The study presents the features of this software category, consisting in benefits for the business environment and their integration into the Internet-of-Things model. Keywords: Big Data, Machine Learning, AIOps, business performance.

Role of artificial intelligence and enterprise risk management to promote corporate entrepreneurship and business performance: evidence from korean banking sector

2020 ◽  
Vol 39 (4) ◽  
pp. 5369-5386
Author(s):  
Hoyoung Lee
Keyword(s):  
Artificial Intelligence ◽  
Information System ◽  
Risk Management ◽  
Business Performance ◽  
Management Practices ◽  
Banking Sector ◽  
Corporate Entrepreneurship ◽  
Partial Least Square ◽  
Equation Modeling ◽  
System Risk

Korean banking industry has achieved significant growth in financial market, however, these banks are lacking with entrepreneurship activities due to low information system risk management. Objective of this study is to examine the effect of artificial intelligence, information system risk management and corporate entrepreneurship on business performance of Korean banks. The current study introduced artificial intelligence as one of the elements to boost risk management activities, corporate entrepreneurship and business performance. This objective was achieved through a research survey among Korean banks. Questionnaires were distributed among the employees of banks by using simple random sampling. Partial Least Square (PLS)-Structural Equation Modeling (SEM) was used for data analysis. Results of the study revealed that artificial intelligence has key role to influence information system risk management. It has positive role to enhance information system risk management practices. Information system risk management practices has vital importance to promote corporate entrepreneurship which increases the business performance of banks. This study is important for Korean banks to make various strategies for risk management, corporate entrepreneurship and business performance.

Artificial intelligence adoption in business-to-business marketing: toward a conceptual framework

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lujie Chen ◽  
Mengqi Jiang ◽  
Fu Jia ◽  
Guoquan Liu
Keyword(s):  
Artificial Intelligence ◽  
Information Processing ◽  
Conceptual Framework ◽  
Conceptual Model ◽  
External Pressure ◽  
Information Processing Theory ◽  
Content Type ◽  
B2b Marketing ◽  
Processing Theory ◽  
Business To Business

Purpose The purpose of this study is to develop a synthesized conceptual framework for artificial intelligence (AI) adoption in the field of business-to-business (B2B) marketing. Design/methodology/approach A conceptual development approach has been adopted, based on a content analysis of 59 papers in peer-reviewed academic journals, to identify drivers, barriers, practices and consequences of AI adoption in B2B marketing. Based on these analyses and findings, a conceptual model is developed. Findings This paper identifies the following two key drivers of AI adoption: the shortcomings of current marketing activities and the external pressure imposed by informatization. Seven outcomes are identified, namely, efficiency improvements, accuracy improvements, better decision-making, customer relationship improvements, sales increases, cost reductions and risk reductions. Based on information processing theory and organizational learning theory (OLT), an integrated conceptual framework is developed to explain the relationship between each construct of AI adoption in B2B marketing. Originality/value This study is the first conceptual paper that synthesizes drivers, barriers and outcomes of AI adoption in B2B marketing. The conceptual model derived from the combination of information processing theory and OLT provides a comprehensive framework for future work and opens avenues of research on this topic. This paper contributes to both AI literature and B2B literature.

scholarly journals Research on enterprise business model and technology innovation based on artificial intelligence

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Sunping Qu ◽  
Hongwei Shi ◽  
Huanhuan Zhao ◽  
Lin Yu ◽  
Yunbo Yu
Keyword(s):  
Artificial Intelligence ◽  
Business Model ◽  
Technological Innovation ◽  
Regression Models ◽  
Business Performance ◽  
Technology Innovation ◽  
Hierarchical Regression ◽  
Model Design ◽  
Innovation Activities ◽  
Business Model Design

AbstractSmall- and medium-sized enterprises (SEMs) are the important part of economic society whose innovation activities are of great significance for building innovative country. In order to investigate how technological innovation (TI) and business model design (BMD) affect the business performance of SMEs, samples of 268 SMEs in the artificial intelligence industry and hierarchical regression models are used in the analysis. The results indicate that TI, BMD, and the matching of them have different effects on the innovation of SMEs of different sizes. These findings are helpful for enriching the theory of the fit between TI and BMD and providing theoretical guidance for the innovation activities in SEMs.

scholarly journals New digital laboratories of experimental knowledge production: Artificial intelligence and education research

2020 ◽  
Vol 18 (2) ◽  
Author(s):  
Ben James Williamson
Keyword(s):  
Artificial Intelligence ◽  
Education Research ◽  
Knowledge Production ◽  
Data Science ◽  
Learning Science ◽  
Data Intensive ◽  
Education Data ◽  
Brain Sciences ◽  
Disciplinary Expertise ◽  
Scientific Expertise

Education data scientists, learning engineers and precision education specialists are new experts in knowledge production in educational research. By bringing together data science methodologies and advanced artificial intelligence (AI) systems with disciplinary expertise from the psychological, biological and brain sciences, they are building a new field of AI-based learning science. This article presents an examination of how education research is being remade as an experimental data-intensive science. AI is combining with learning science in new ‘digital laboratories’ where ownership over data, and power and authority over educational knowledge production, are being redistributed to research assemblages of computational machines and scientific expertise.

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