1st Workshop on Amalgamating Artificial Intelligence and Business Innovation: Creating Value in the New Normal Era (AAIBI 2021)


Nowadays, with the unprecedented and exponential growth of information communication technologies (ICTs), all types of businesses have been impacted by digital transformation. For instance, blockchain, big data, artificial intelligence, and Industry 4.0 are not only buzzwords but were in the top search interest in 2018 between scholars (Nature, 2019). Digital transformation (DT) is driven by enhanced connectivity and an explosion of available data changing the way enterprises do business (Davenport, 2019; Rai, Constantinides, & Sarker, 2019). This frequently implies new strategies, new business models, and new and dynamic capabilities, mostly to create data-driven businesses. In this context, organizations are being challenged to understand and create value using cutting-edge technologies. Also, due to the unprecedented COVID-19 epidemic outbreak (Queiroz, Ivanov, Dolgui, & Fosso Wamba, 2020), the information systems field exerts a fundamental role in organizations and society during and after the crisis. In this outlook, a prominent a highly disruptive technology, Artificial Intelligence hereinafter AI, defined as “the ability of machines to mimic intelligent human behavior, and specifically refers to “cognitive” functions that we associate with the human mind, including problem-solving and learning” (Syam & Sharma, 2018, p. 136). In addition, firms are investing in AI approaches, like machine learning (ML), in order to empower the capacity of the robots and algorithms to learn with the experience. With the use of ML, the organizations are the strength of the self-decision making the process of the robots for a vast of activities (e.g., customer support in a call center, interaction in the social media, product recommendation, patterns recognition, travel recommendations, follow-up, activities, etc.). In this context, organizations and their supply chain management (SCM) are experimenting with significant challenges to applying AI effectively into their business models (Davenport, 2019; Plastino & Purdy, 2018). For example, AI organizations make use of “Artificial Intelligence” in supply chain ecosystems, which in combination with human behavior, will create a new degree of intelligence, innovation, and collaboration” (Bienhaus, Haddud, Bienhaus, & Haddud, 2018, p. 966). As potential examples, AI and ML can be used to detect card fraud in payments (Ryman-tubb, Krause, & Garn, 2018), in sales management improvement (Syam & Sharma, 2018), in procurement and supply chains (Bienhaus et al., 2018). With AI, organizations will need to adopt new business models and approach in their functional areas (Plastino & Purdy, 2018) and need to rethink the work in the age of intelligent machines and AI (Aleksander, 2017). Unfortunately, the organizations have limited awareness about the effects of AI in their business models and how these can affect their business management to create value. Moreover, empirical studies covering cutting-edge technologies for business value creation and capture it are scarce (Fosso Wamba, Kala Kamdjoug, Epie Bawack, & Keogh, 2020). Additionally, the existing literature is also limited in reporting the AI strategies and its Interplay with other technologies, in both organizational and SCM levels. Furthermore, organizations need to explore more in-depth the interaction of AI and human intelligence (Kelling et al., 2015). Therefore, this Workshop aims to invite scholars, researchers, practitioners, and managers to shed more light, unlock, and identify at the organizational, inter-organizational and SCM levels, the dynamics in capture business value from AI and the Interplay with others emerging technologies, in terms of improved performance, innovative business models, improved decisions making improved interaction with customers, and competitive advantage.


Topics of interest:

We encourage submissions employing empirical methods (e.g., surveys, in-depth cases, pilot studies, mixed methods, etc.) but are not limited to the following topics:

·       AI innovative approaches to support organizations during and after a crisis.

·       AI innovative approaches to improve social good in the “new normal” era.

·       Determinants of AI adoption and use in operations at the organizational and inter-organizational levels.

·       AI and intelligent agents supporting business process management.

·       The role of AI for the digital transformation: awareness and knowledge challenges in emerging and developed countries.

·       AI, data analytics and intelligent sensors supporting optimization for smart manufacturing and Industry 4.0.

·       AI and the Interplay with cutting-edge technologies (e.g., Blockchain/BDA/CPS/IoT, among others) enabled-business process innovation at the firm and supply chain levels.

·       Determinants of the AI diffusion stages (intention, adoption, and routinization) in supply chains

·       AI initiatives for creating value and competitive advantage. Assessment of facilitators and inhibitors of AI adoption for supply chain management processes.

·       AI initiatives reporting performance improved, competitive advantage, and business value at the organizational and inter-organizational levels.

·       Implementation of IT infrastructure to support AI initiatives for improved operations management, lean & agile operations, quality management in operations and SCM.

·       AI supporting human interaction and collaboration within organizations.

·       Facilitation of innovative electronic business models and operations by using AI techniques in various sectors (e.g., transportation, fashion, healthcare, retail industry, and manufacturing).

·       Ethics issues about AI usage


Organizing Committee:

·       Samuel Fosso Wamba Toulouse Business School - TBS, Information, Operations and Management Sciences, Toulouse, France, This email address is being protected from spambots. You need JavaScript enabled to view it.

·       Maciel M. Queiroz Paulista University – UNIP, Brazil, This email address is being protected from spambots. You need JavaScript enabled to view it.


Program Committee:

·       Samuel Fosso Wamba, Toulouse Business School, France

·       Maciel M. Queiroz, Paulista University - UNIP, Brazil

·       Shahriar Akter, Sydney Business School, Faculty of Business, University of Wollongong, Australia

·       Jean Robert Kala Kamdjoug, Information System, Catholic University of Central University, Cameroon

·       Bendavid Ygal, Département de management et technologie, UQAM, Canada

·       Rameshwar Dubey, Montpellier Business School, France

·       Mithu Bhattacharya, College of Business Administration, University of Detroit Mercy, USA

·       Mary Dunaway, Information Science and Systems, School of Business & Management, Morgan State University, USA

·       Sullivan Yulia, Hankamer School of Business, Baylor University, USA

·       Kim Tan, Nottingham University Business School, University of Nottingham, UK

·       Alexandre Moise, Université de Sherbrooke, Canadá

·       Vincent Dutot, IPAG Business School, França

·      Donald Newmann, Boticario, Brazil

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