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Overview: Digital transformation is reshaping entire segments and industries: communications, retail, and, increasingly, health care, medicine, agriculture, and manufacturing. While a few companies reach front-runner status, most seem to lag. Digital transformation is a top concern of senior leaders worldwide and motivated the development of this study. This article describes the results of the Patterns of Digitization survey designed to assess how companies are implementing digital transformation. The survey covers the various strategies companies employ, the technologies they invest in, and, in particular, the actions they take to overcome the organizational resistance that is common in most large-scale transformations. We highlight important actions all companies are taking to digitally transform their businesses and the differentiated actions of digitally mature organizations. The insights gleaned from the study should help lagging companies understand what is involved in implementing a digital transformation and what they need to do to catch up.
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... We first studied the phenomenon of digital transformation and the role digital leaders play through an extensive survey of global organizations. Called Patterns of Digitization [6], the survey examined every aspect of how digital transformation is implemented. We looked at over 500 companies, their business strategies, resource allocation, design practices, etc. ...
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Many digital leaders are currently dealing with expanding into the highly sought-after "holy grail" of digitization by deploying Artificial Intelligence (AI) solutions. While AI projects of all types are being launched prolifically, clouds of doubt are forming around the credibility of AI results. AI has many potential benefits for businesses, but these will be realized only if people trust these tools to produce unbiased results. Managing transformation in the digital age requires leaders who not only understand the potential of these tools but also possess the necessary human skills to reshape the organization. This paper addresses one particular human dimension in some depth-ethical AI-and the actions successful digital leaders take to build trust in its results. It introduces a conceptual model of ethical AI considerations to ensure the use of this potentially lucrative, yet disruptive technology is indeed fair, explainable, and productive.
... Some papers aimed to strengthen theories by proposing frameworks and patterns related to the "data-driven business model" (Culot et al., 2020;Jabbour et al., 2019;Sorescu, 2017). Other studies attempted to define established firms' challenges in creating new digitally-based business models and having a digital transformation in their organizations (Mugge et al., 2020;Nguyen Dang Tuan et al., 2019;Ritter & Pedersen, 2020). Finally, there are other works published that are interested in recognizing different value capturing strategies from this massive amount of data through multiple or "twosided platforms" regarding the customers' perspectives Hartmann et al., 2016;McColl-Kennedy et al., 2019;Trabucchi et al., 2017b;Urbinati et al., 2019). ...
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The emergence of big data is a radical shift in the business context, leading to a change in value creation and capture. This phenomenon is a newborn concept in the business and management literature confirmed by the growing number of publications over recent years. This paper presents an updating comprehensive bibliometric analysis to describe and assess the scientific landscape of value creation and capture based on leveraging big data in the literature. Bibliometrix and VOSviewer were selected as software tools for descriptive and network bibliometric analysis based on the Web of Science Core Collection database from 2011 till 2020. By implementing bibliometric analysis such as analysis of citations and co-occurrence of keywords, we have recognized the most prominent and influential authors, papers, journals, countries, and four potential clusters of current trends in studies. These four trends of value creation and capture from big data studies are: 1) strengthening the basic knowledge of value creation in the big data era, 2) data-driven business model and value capturing, 3) dynamic capabilities and centrality of knowledge, and 4) digital transformation of the service industry. Finally, by identifying the existing research gaps, future research directions in each cluster are demonstrated.
... The previous section shows that there is an increasing awareness of the need for a better understanding of the interplay between digital technologies and the social and physical context in which these technologies are used. This has been researched across a broad variety of sectors such as health, textile, manufacturing, energy, and transport (Balasubramanian, Shukla, Sethi, Islam, & Saloum, 2021;Fromhold-Eisebith, Marschall, Peters, & Thomes, 2021;Kiel, Arnold, & Voigt, 2017;Kolloch & Dellermann, 2018;Mugge, Abbu, Michaelis, Kwiatkowski, & Gudergan, 2020;Myskja & Steinsbekk, 2020;Sraml Gonzalez & Gulbrandsen, 2021;Tijan, Jović, Aksentijević, & Pucihar, 2021); from many different technological and social perspectives (e.g. adoption, trust, governance, ethics) (Hansen et al., 2020;Jakku et al., 2019;van den Broek & van Veenstra, 2018;van der Burg et al., 2019a); at different system 'levels' and perspectives (e.g. ...
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