Analytical foundations for development of real-time supply chain capabilities
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[1] Murtaza Haider,et al. Beyond the hype: Big data concepts, methods, and analytics , 2015, Int. J. Inf. Manag..
[2] Thomas H. Davenport,et al. How strategists use “big data” to support internal business decisions, discovery and production , 2014 .
[3] Desirée Knoppen,et al. A comprehensive assessment of measurement equivalence in operations management , 2015 .
[4] Brent D. Williams,et al. Leveraging supply chain visibility for responsiveness: The moderating role of internal integration , 2013 .
[5] Dursun Delen,et al. Leveraging the capabilities of service-oriented decision support systems: Putting analytics and big data in cloud , 2013, Decis. Support Syst..
[6] Tarikere T. Niranjan,et al. Measuring information distortion in real-world supply chains , 2011 .
[7] Νικόλαος Γ. Θερίος,et al. Organizational learning as a strategic resource in supply chain management , 2006 .
[8] Tobias Schoenherr,et al. Revisiting the arcs of integration: Cross-validations and extensions , 2012 .
[9] E. Bulut,et al. The Use of Partial Least Squares Path Modeling in Investigating the Relationship between Leadership, Motivation and Rewarding , 2015 .
[10] A. Tversky,et al. Prospect theory: an analysis of decision under risk — Source link , 2007 .
[11] Paul Benjamin Lowry,et al. Partial Least Squares (PLS) Structural Equation Modeling (SEM) for Building and Testing Behavioral Causal Theory: When to Choose It and How to Use It , 2014, IEEE Transactions on Professional Communication.
[12] Jeanne G. Harris,et al. Talent and analytics: new approaches, higher ROI , 2011 .
[13] Melnned M. Kantardzic. Big Data Analytics , 2013, Lecture Notes in Computer Science.
[14] Kathleen M. Eisenhardt,et al. DYNAMIC CAPABILITIES, WHAT ARE THEY? , 2000 .
[15] A. Tversky,et al. Judgment under Uncertainty: Heuristics and Biases , 1974, Science.
[16] Kevin McCormack,et al. Improving performance aligning business analytics with process orientation , 2013, Int. J. Inf. Manag..
[17] Bongsik Shin,et al. Data quality management, data usage experience and acquisition intention of big data analytics , 2014, Int. J. Inf. Manag..
[18] Thurasamy Ramayah,et al. Testing and Controlling for Common Method Variance: A Review of Available Methods , 2017 .
[19] T. Davenport. big data @ work , 2014 .
[20] R Core Team,et al. R: A language and environment for statistical computing. , 2014 .
[21] Matthias Jarke,et al. Ontology-Based Data Quality Management for Data Streams , 2016, ACM J. Data Inf. Qual..
[22] Ken G. Smith,et al. Organizational Information Processing, Competitive Responses, and Performance in the U.S. Domestic Airline Industry , 1991 .
[23] Clyde W. Holsapple,et al. A unified foundation for business analytics , 2014, Decis. Support Syst..
[24] Marko Sarstedt,et al. Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research , 2014 .
[25] Veda C. Storey,et al. Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..
[26] Scott B. MacKenzie,et al. Common method biases in behavioral research: a critical review of the literature and recommended remedies. , 2003, The Journal of applied psychology.
[27] Diane M. Strong,et al. Beyond Accuracy: What Data Quality Means to Data Consumers , 1996, J. Manag. Inf. Syst..
[28] Klaus Moeller,et al. Performance Management Analytics - The Next Extension in Managerial Accounting , 2010 .
[29] Thomas Redman,et al. The impact of poor data quality on the typical enterprise , 1998, CACM.
[30] Riccardo Silvi,et al. A framework for business analytics in performance management , 2012 .
[31] Marco Antonsich. BRINGING THE DEMOS BACK IN , 2012 .
[32] Ron Kohavi,et al. Emerging trends in business analytics , 2002, CACM.
[33] S. Leung. A Comparison of Psychometric Properties and Normality in 4-, 5-, 6-, and 11-Point Likert Scales , 2011 .
[34] Ronald K. Klimberg,et al. Benchmarking Academic Programs in Business Analytics , 2014, Interfaces.
[35] Christian F. Durach,et al. Mapping the Landscape of Future Research Themes in Supply Chain Management , 2016 .
[36] Peter T. Ward,et al. Impact of Information Technology Integration and Lean/Just-In-Time Practices on Lead-Time Performance , 2006, Decis. Sci..
[37] Wolfgang Albrecht,et al. Continuous-time production, distribution and financial planning with periodic liquidity balancing , 2017, J. Sched..
[38] Sean B. Maynard,et al. Towards a business analytics capability maturity model , 2012 .
[39] M. Holweg,et al. Creating the customer‐responsive supply chain: a reconciliation of concepts , 2007 .
[40] Dursun Delen,et al. Data, information and analytics as services , 2013, Decis. Support Syst..
[41] M. Frohlich,et al. Arcs of integration: an international study of supply chain strategies , 2001 .
[42] Peter B. Seddon,et al. How Does Business Analytics Contribute to Business Value? , 2012, ICIS.
[43] Morgan Swink,et al. How the Use of Big Data Analytics Affects Value Creation in Supply Chain Management , 2015, J. Manag. Inf. Syst..
[44] A. Tversky,et al. Judgment under Uncertainty , 1982 .
[45] Anna Sidorova,et al. Business intelligence success: The roles of BI capabilities and decision environments , 2013, Inf. Manag..
[46] David E. Cantor,et al. Maximizing the Potential of Contemporary Workplace Monitoring: Techno‐Cultural Developments, Transactive Memory, and Management Planning , 2016 .
[47] Jeanne G. Harris,et al. Competing on Analytics: The New Science of Winning , 2007 .
[48] T. Davenport,et al. Data to Knowledge to Results: Building an Analytic Capability , 2001 .
[49] T. Davenport,et al. Data scientist: the sexiest job of the 21st century. , 2012, Harvard business review.
[50] Marlena J. Gaul. Big Data at Work: Dispelling the Myths, Uncovering the Opportunities , 2014 .
[51] Richard L. Daft,et al. Organizational information requirements, media richness and structural design , 1986 .
[52] Roy van Beest. Project Intelligence: is Data Analytics the new path to value? , 2016 .
[53] Benjamin T. Hazen,et al. Data quality for data science, predictive analytics, and big data in supply chain management: An introduction to the problem and suggestions for research and applications , 2014 .
[54] Tom Linton,et al. The LIVING Supply Chain , 2017 .
[55] Sam Ransbotham,et al. Beyond the hype: The hard work behind analytics success , 2016 .
[56] Rick Dove. Agile Enterprise Cornerstones: Knowledge, Values, and Response Ability , 2005, Business Agility and Information Technology Diffusion.
[57] Jan Emblemsvåg,et al. Business analytics: getting behind the numbers , 2005 .
[58] M. Overmyer. Back to business , 1984, BMJ : British Medical Journal.
[59] Wolfgang Albrecht,et al. Coordinating continuous-time distribution and sales planning of perishable goods with quality grades , 2018 .
[60] Vijay Khatri,et al. Business analytics: Why now and what next? , 2014 .
[61] K. Moeller,et al. Integrating business analytics into strategic planning for better performance , 2011 .
[62] Marco Torchiano,et al. Open data quality measurement framework: Definition and application to Open Government Data , 2016, Gov. Inf. Q..
[63] A. C. Lyons *,et al. Prototyping an information system's requirements architecture for customer-driven, supply-chain operations , 2005 .
[64] Enzo Morosini Frazzon,et al. Hybrid approach for the integrated scheduling of production and transport processes along supply chains , 2018, Int. J. Prod. Res..
[65] Ranjit Bose,et al. Advanced analytics: opportunities and challenges , 2009, Ind. Manag. Data Syst..
[66] Sam Ransbotham,et al. Minding the analytics gap , 2015 .
[67] Robert B. Handfield,et al. Preparing for the Era of the Digitally Transparent Supply Chain: A Call to Research in a New Kind of Journal , 2016 .
[68] Gary Cokins. Mining the past to see the future: CFOs and their teams can use business analytics to make better decisions , 2014 .
[69] Graeme G. Shanks,et al. Achieving benefits with business analytics systems: an evolutionary process perspective , 2012, J. Decis. Syst..
[70] Michael zur Muehlen,et al. Business Process Analytics , 2015, Handbook on Business Process Management.
[71] Clyde W. Holsapple,et al. Exploring secondary activities of the knowledge chain , 2005 .
[72] Neil F. Doherty,et al. Operational research from Taylorism to Terabytes: A research agenda for the analytics age , 2015, Eur. J. Oper. Res..
[73] Helmut Krcmar,et al. Big Data , 2014, Wirtschaftsinf..
[74] Jennifer Blackhurst,et al. The Severity of Supply Chain Disruptions: Design Characteristics and Mitigation Capabilities , 2007, Decis. Sci..
[75] J. Scott Armstrong,et al. Estimating nonresponse bias in mail surveys. , 1977 .
[76] Clyde W. Holsapple,et al. A unified model of supply chain agility : the work-design perspective , 2008 .
[77] Paul D. Cousins,et al. HOW CAN SUPPLY MANAGEMENT REALLY IMPROVE PERFORMANCE? A KNOWLEDGE-BASED MODEL OF ALIGNMENT CAPABILITIES , 2015 .
[78] Jill Eicher,et al. Business Process Analytics , 2007 .
[79] Arun Rai,et al. Firm performance impacts of digitally enabled supply chain integration capabilities , 2006 .
[80] Rohit Nishant,et al. Do shareholders favor business analytics announcements? , 2016, J. Strateg. Inf. Syst..
[81] A. Tversky,et al. Prospect theory: analysis of decision under risk , 1979 .
[82] Rudolf R. Sinkovics,et al. The Use of Partial Least Squares Path Modeling in International Marketing , 2009 .
[83] Marshall L. Fisher,et al. Supply Chain Inventory Management and the Value of Shared Information , 2000 .
[84] E. L. Nichols,et al. ORGANIZATIONAL LEARNING AS A STRATEGIC RESOURCE IN SUPPLY MANAGEMENT , 2003 .
[85] L. Burns,et al. Adoption and abandonment of matrix management programs: effects of organizational characteristics and interorganizational networks. , 1993, Academy of Management journal. Academy of Management.
[86] Jay R. Galbraith. Organization Design: An Information Processing View , 1974 .
[87] Marko Sarstedt,et al. Multigroup Analysis in Partial Least Squares (PLS) Path Modeling: Alternative Methods and Empirical Results , 2011 .
[88] Cleotilde Gonzalez,et al. How analytic reasoning style and global thinking relate to understanding stocks and flows , 2015 .
[89] A. Oke,et al. Antecedents of supply chain visibility in retail supply chains: A resource-based theory perspective , 2007 .