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Tesi etd-03012022-230510


Tipo di tesi
Tesi di dottorato di ricerca
Autore
CAPPELLI, ANDREA
URN
etd-03012022-230510
Titolo
The Role of Big Data Analytics Capabilities in the Yachting Industry: Empirical Evidence from Tuscany
Settore scientifico disciplinare
SECS-P/07
Corso di studi
ECONOMIA AZIENDALE E MANAGEMENT
Relatori
tutor Prof. Castellano, Nicola Giuseppe
Parole chiave
  • Big data analytics capabilities
  • TND
  • Competitive advantage
Data inizio appello
07/03/2022
Consultabilità
Non consultabile
Data di rilascio
07/03/2062
Riassunto
The discussion around big data and business analytics has increased significantly in the last decade and a key challenge for organizations is to understand how to leverage the potential related to these new information technologies in order to create business value and competitive performance gains. However, although literature recognizes the relevance of big data and related 4.0-enabling technologies, it mainly focused on technical and technological aspects, with little regard to the human and intangible resources required to effectively turn big data investments into value at the organizational-level. Only recently big data literature shifted its attention towards the organizational changes that organizations should undertake in order to harvest the full benefits from their big data analytics investments, by introducing the concept of big data analytics capability. Indeed, in several cases across various industries worldwide, the implementation of big data initiatives hurt rather than helped companies to achieve competitive and financial performance gains: in fact, big data is not a “panacea” and multiple aspects should be considered when exploring the potential of big data investments. In addition, the mechanisms through which companies may derive any business value from big data also depend on the industry and specific context where such a information technology is applied.
For this reason, the present research focuses on the yachting domain in order to contribute to existing literature and to provide practitioners with useful advice to make their firms’ information systems more accurate, faster, and aligned with the information needs. Preliminarily, we conduct a narrative and anecdotal literature review in order to understand if the adoption of big data analytics is considered by academics and practitioners as a critical success factor that firms of the yachting industry should take into account when shaping their “competitive strategy for tomorrow”, as well as the main activities of the value chain that can benefit the most from the implementation of big data projects. Then, we define the theoretical background of the research that is grounded on IT-capability literature, which derives its main assumptions from RBV and DCV. On the basis of the theoretical background, we conduct a literature review concerning big data analytics capability in order to clarify the main resources and competencies required for developing and enhancing the firm’s ability to exploit its technological and tangible big data assets. Past IT-capability and recent big data literature led scholars to coin this notion, as investments in information technologies per se do not generate business value, and firms have to develop idiosyncratic and specific capabilities to achieve such a goal. Drawing on RBV, DCV, and IT-capability literature, this research investigates in the Tuscan Nautical District the critical “building blocks” that are required to develop a big data analytics capability, it explores the organizational capabilities enabled by such a capability, and it determines enabling and inhibiting factors that may condition the value created by big data investments. Through a multiple-case study, conducted in the Tuscan Nautical District, we explore (i) the managerial challenges that companies of the district, which are planning to invest in big data, should pay attention to, (ii) the potential of big data to support companies of the district to achieve and sustain a competitive edge, (iii) the establishment of big data-knowledge sharing strategies in the district and the strategies to incentivize the creation of knowledge synergies. Finally, we discuss our findings in relation to the literature reviews performed, by providing academics and practitioners with a contribution to better understand the potential of big data in a specific-industry domain.
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