LEADER 02786nam 22004093a 450 001 9910831849203321 005 20250203235540.0 010 $a9783863096694 010 $a386309669X 024 8 $ahttps://doi.org/10.20378/irbo-54833 035 $a(CKB)4950000000290237 035 $a(ScCtBLL)33be3fb7-89e7-435d-a5c0-6fc564fcf52f 035 $a(Perlego)2327436 035 $a(EXLCZ)994950000000290237 100 $a20250203i20192021 uu 101 0 $aeng 135 $auru|||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aPredictive Analytics for Energy Efficiency and Energy Retailing$fKonstantin Hopf$hVolume 36 210 1$a[s.l.] :$cBamberg University Press,$d2019. 215 $a1 online resource (1 p.) 225 1 $aSchriften aus der Fakultät Wirtschaftsinformatik und Angewandte Informatik 330 $aDigitization causes large amounts of data in organizations (e.g., transaction data from business processes, communication data, sensor data). Besides, a large number of data sources are emerging online and can be freely used. Firms are looking for ways to commercialize this increasing amount of data and research aims to better understand the data value creation process. The present dissertation answers five central research questions in this context and examines how machine learning (ML) can be used to create value from data, using case studies from energy retailing and energy efficiency. First, a systematic literature review gives an overview of firm internal and external data sources for potential analyses. Second, the importance of human cognition, theory, and expert knowledge in effective data preparation for ML is demonstrated. Third, current ML algorithms and variable selection methods are empirically compared using industry data sets. Implications for theory and practice are identified. Finally, the successful use of the information gained through ML is exemplified through case studies where increased energy efficiency, customer value, and service quality can demonstrate economic, environmental, and social value. Thus, this empirical work contributes to the so far rather conceptual discussion on value creation from big data in information systems research. 410 $aSchriften aus der Fakultät Wirtschaftsinformatik und Angewandte Informatik 606 $aComputers / Software Development & Engineering$2bisacsh 606 $aComputers 615 7$aComputers / Software Development & Engineering 615 0$aComputers. 700 $aHopf$b Konstantin$01786935 801 0$bScCtBLL 801 1$bScCtBLL 906 $aBOOK 912 $a9910831849203321 996 $aPredictive Analytics for Energy Efficiency and Energy Retailing$94319467 997 $aUNINA