Towards a business analytics capability for the circular economy

Abstract Digital technologies are growing in importance for accelerating firms’ circular economy transition. However, so far, the focus has primarily been on the technical aspects of implementing these technologies with limited research on the organizational resources and capabilities required for successfully leveraging digital technologies for circular economy. To address this gap, this paper explores the business analytics resources firms should develop and how these should be orchestrated towards a firm-wide capability. The paper proposes a conceptual model highlighting eight business analytics resources that, in combination, build a business analytics capability for the circular economy and how this relates to firms’ circular economy implementation, resource orchestration capability, and competitive performance. The model is based on the results of a thematic analysis of 15 semi-structured expert interviews with key positions in industry. Our approach is informed by and further develops, the theory of the resource-based view and the resource orchestration view. Based on the results, we develop a deeper understanding of the importance of taking a holistic approach to business analytics when leveraging data and analytics towards a more efficient and effective digital-enabled circular economy, the smart circular economy.

[1]  J. Wincent,et al.  A Definition and Theoretical Review of the Circular Economy, Value Creation, and Sustainable Business Models: Where Are We Now and Where Should Research Move in the Future? , 2018, Sustainability.

[2]  Davide Chiaroni,et al.  Circular industry 4.0: an integrative framework , 2018 .

[3]  Gustavo Cattelan Nobre,et al.  Scientific literature analysis on big data and internet of things applications on circular economy: a bibliometric study , 2017, Scientometrics.

[4]  N. Saccani,et al.  The role of digital technologies to overcome Circular Economy challenges in PSS Business Models: an exploratory case study , 2018 .

[5]  Nancy Bocken,et al.  Towards a sufficiency-driven business model : Experiences and opportunities , 2016 .

[6]  Clint Chadwick,et al.  Resource orchestration in practice: CEO emphasis on SHRM, commitment‐based HR systems, and firm performance , 2015 .

[7]  S. Al-Athel,et al.  Report of the World Commission on Environment and Development: "Our Common Future" , 1987 .

[8]  S. K. Johl,et al.  Big Data Analytics Capabilities and Eco-Innovation: A Study of Energy Companies , 2019, Sustainability.

[9]  Angappa Gunasekaran,et al.  The impact of big data on world-class sustainable manufacturing , 2015, The International Journal of Advanced Manufacturing Technology.

[10]  A. Heshmati,et al.  A review of the circular economy in China : Moving from rhetoric to implementation , 2013 .

[11]  Benjamin T. Hazen,et al.  Circular economy and big data analytics: A stakeholder perspective , 2019, Technological Forecasting and Social Change.

[12]  Claas Henning Wilts,et al.  The digital circular economy : can the digital transformation pave the way for resource-efficient materials cycles? , 2017 .

[13]  Sarah J. Tracy Qualitative Quality: Eight “Big-Tent” Criteria for Excellent Qualitative Research , 2010 .

[14]  Arun Rai,et al.  Firm performance impacts of digitally enabled supply chain integration capabilities , 2006 .

[15]  A. Gunasekaran,et al.  Can big data and predictive analytics improve social and environmental sustainability? , 2017, Technological Forecasting and Social Change.

[16]  Shaker A. Zahra,et al.  The Net-Enabled Business Innovation Cycle and the Evolution of Dynamic Capabilities , 2002, Inf. Syst. Res..

[17]  Caroline Stenbacka Qualitative research requires quality concepts of its own , 2001 .

[18]  Paul A. Pavlou,et al.  Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities , 2020, Inf. Manag..

[19]  B. Wernerfelt,et al.  A Resource-Based View of the Firm , 1984 .

[20]  Roger H. L. Chiang,et al.  Big Data Research in Information Systems: Toward an Inclusive Research Agenda , 2016, J. Assoc. Inf. Syst..

[21]  M. Savin-Baden,et al.  Qualitative Research: The essential guide to theory and practice , 2012 .

[22]  A. Tiwari,et al.  Digitisation and the Circular Economy: A Review of Current Research and Future Trends , 2018, Energies.

[23]  Denis Kurle,et al.  A Big Data Analytics Approach to Develop Industrial Symbioses in Large Cities , 2015 .

[24]  Ning Zhang,et al.  An optimization model for green supply chain management by using a big data analytic approach , 2017 .

[25]  S. Ulgiati,et al.  A review on circular economy: the expected transition to a balanced interplay of environmental and economic systems , 2016 .

[26]  Nahid Golafshani,et al.  Understanding Reliability and Validity in Qualitative Research , 2003 .

[27]  Ken Webster,et al.  What Might We Say about a Circular Economy? Some Temptations to Avoid if Possible , 2013 .

[28]  M. Patton Qualitative Research & Evaluation Methods: Integrating Theory and Practice , 2014 .

[29]  Tom Fawcett,et al.  Data science for business , 2013 .

[30]  Yajiong Xue,et al.  Boundary‐spanning search and firms' green innovation: The moderating role of resource orchestration capability , 2020, Business Strategy and the Environment.

[31]  Chetna Chauhan,et al.  A SAP-LAP linkages framework for integrating Industry 4.0 and circular economy , 2019, Benchmarking: An International Journal.

[32]  M. Ormazábal,et al.  Key strategies, resources, and capabilities for implementing circular economy in industrial small and medium enterprises , 2019, Corporate Social Responsibility and Environmental Management.

[33]  Constance E. Helfat,et al.  Dynamic capabilities : understanding strategic change in organizations , 2007 .

[34]  A. Braganza,et al.  Resource management in big data initiatives: Processes and dynamic capabilities , 2017 .

[35]  Fabio Iraldo,et al.  The role of dynamic capabilities in circular economy implementation and performance of companies , 2020 .

[36]  E. Hultink,et al.  The Circular Economy - A New Sustainability Paradigm? , 2017 .

[37]  Imed Boughzala,et al.  The effect of Big Data Analytics Capability on Firm Performance , 2016, PACIS.

[38]  Shahriar Akter,et al.  Big data analytics and firm performance: Effects of dynamic capabilities , 2017 .

[39]  Giacomo Marzi,et al.  Big data and dynamic capabilities: a bibliometric analysis and systematic literature review , 2019, Management Decision.

[40]  Paul A. Pavlou,et al.  From IT Leveraging Competence to Competitive Advantage in Turbulent Environments: The Case of New Product Development , 2006, Inf. Syst. Res..

[41]  Daniel J. Power,et al.  Data science: supporting decision-making , 2016, J. Decis. Syst..

[42]  J. Barney Resource-based theories of competitive advantage: A ten-year retrospective on the resource-based view , 2001 .

[43]  Patrick Mikalef,et al.  Big Data Analytics Capability: Antecedents and Business Value , 2017, PACIS.

[44]  P. H. Friesen,et al.  A Longitudinal Study of the Corporate Life Cycle , 1984 .

[45]  Yvonna S. Lincoln,et al.  Judging the quality of case study reports , 1990 .

[46]  J. Nußholz,et al.  A Review and Evaluation of Circular Business Model Innovation Tools , 2019, Sustainability.

[47]  J. Wincent,et al.  Digitalization Capabilities as Enablers of Value Co‐Creation in Servitizing Firms , 2017 .

[48]  Angappa Gunasekaran,et al.  IoT powered servitization of manufacturing – an exploratory case study , 2017 .

[49]  David G. Sirmon,et al.  Managing Firm Resources in Dynamic Environments to Create Value: Looking Inside the Black Box , 2007 .

[50]  Bongsik Shin,et al.  Data quality management, data usage experience and acquisition intention of big data analytics , 2014, Int. J. Inf. Manag..

[51]  Peter B. Seddon,et al.  How Does Business Analytics Contribute to Business Value? , 2012, ICIS.

[52]  Paolo Rosa,et al.  Assessing relations between Circular Economy and Industry 4.0: a systematic literature review , 2019, Int. J. Prod. Res..

[53]  Michail N. Giannakos,et al.  Big data analytics capabilities: a systematic literature review and research agenda , 2017, Information Systems and e-Business Management.

[54]  C. Seale Quality in Qualitative Research , 1999 .

[55]  P. Planing Business Model Innovation in a Circular Economy Reasons for Non-Acceptance of Circular Business Models , 2015 .

[56]  J. Barney Firm Resources and Sustained Competitive Advantage , 1991 .

[57]  Donald E. Brown,et al.  Future trends in business analytics and optimization , 2011, Intell. Data Anal..

[58]  Ioannis G. Askoxylakis A Framework for Pairing Circular Economy and the Internet of Things , 2018, 2018 IEEE International Conference on Communications (ICC).

[59]  Stéphane Bressan,et al.  A Collaboration Platform for Enabling Industrial Symbiosis: Application of the Database Engine for Waste-to-Resource Matching , 2018 .

[60]  Michael Lieder,et al.  A choice behavior experiment with circular business models using machine learning and simulation modeling , 2020, Journal of Cleaner Production.

[61]  Tim C. McAloone,et al.  The Emergent Role of Digital Technologies in the Circular Economy: A Review , 2017 .

[62]  Terence R. Mitchell,et al.  Top Level Management Priorities in Different Stages of the Organizational Life Cycle , 1985 .

[63]  S. Kvale,et al.  InterViews: Learning the Craft of Qualitative Research Interviewing , 1996 .

[64]  Heinz-Theo Wagner,et al.  Towards a Conceptualization of Data Analytics Capabilities , 2016, 2016 49th Hawaii International Conference on System Sciences (HICSS).

[65]  J. Creswell,et al.  Determining Validity in Qualitative Inquiry , 2000 .

[66]  Neil F. Doherty,et al.  Operational research from Taylorism to Terabytes: A research agenda for the analytics age , 2015, Eur. J. Oper. Res..

[67]  S. Choi,et al.  Entrepreneurial Orientation, Resource Orchestration Capability, Environmental Dynamics and Firm Performance: A Test of Three-Way Interaction , 2020 .

[68]  Thayla T. Sousa-Zomer,et al.  Exploring the challenges for circular business implementation in manufacturing companies: An empirical investigation of a pay-per-use service provider , 2017, Resources, Conservation and Recycling.

[69]  Veda C. Storey,et al.  Business Intelligence and Analytics: From Big Data to Big Impact , 2012, MIS Q..

[70]  Erik Brynjolfsson,et al.  Big data: the management revolution. , 2012, Harvard business review.

[71]  M. Janssen,et al.  Factors influencing big data decision-making quality , 2017 .

[72]  Vasant Dhar,et al.  Data science and prediction , 2012, CACM.

[73]  Patrick Mikalef,et al.  Exploring the Relationship Between Data Science and Circular Economy: An Enhanced CRISP-DM Process Model , 2019, I3E.

[74]  Charbel José Chiappetta Jabbour,et al.  Industry 4.0 and the circular economy: a proposed research agenda and original roadmap for sustainable operations , 2018, Annals of Operations Research.

[75]  R. Duane Ireland,et al.  Resource Orchestration to Create Competitive Advantage , 2010 .

[76]  Niraj Kumar,et al.  Understanding big data analytics capabilities in supply chain management: Unravelling the issues, challenges and implications for practice , 2017, Transportation Research Part E: Logistics and Transportation Review.

[77]  Gerd Kortuem,et al.  Circular Strategies Enabled by the Internet of Things—A Framework and Analysis of Current Practice , 2019, Sustainability.

[78]  Hamza Alshenqeeti,et al.  Interviewing as a Data Collection Method: A Critical Review , 2014 .

[79]  W. Stahel The Performance Economy , 2010 .

[80]  Suchit Ahuja,et al.  Resource Orchestration for IT-enabled Innovation , 2017 .

[81]  Shahriar Akter,et al.  How to improve firm performance using big data analytics capability and business strategy alignment , 2016 .

[82]  Patrick Mikalef,et al.  The smart circular economy: A digital-enabled circular strategies framework for manufacturing companies , 2020 .

[83]  Benjamin T. Hazen,et al.  Big data and predictive analytics for supply chain sustainability: A theory-driven research agenda , 2016, Comput. Ind. Eng..

[84]  Robert K. Yin,et al.  Case Study Research and Applications: Design and Methods , 2017 .

[85]  Federico Adrodegari,et al.  Exploring How Usage-Focused Business Models Enable Circular Economy through Digital Technologies , 2018 .

[86]  M. Hekkert,et al.  Conceptualizing the Circular Economy: An Analysis of 114 Definitions , 2017 .

[87]  William L. Fuerst,et al.  Information technology and sustained competitive advantage: a resource-based analysis , 1995 .

[88]  N. Bocken,et al.  Why Do Companies Pursue Collaborative Circular Oriented Innovation? , 2019, Sustainability.

[89]  Michael D. Myers,et al.  The qualitative interview in IS research: Examining the craft , 2007, Inf. Organ..

[90]  Kathleen M. Eisenhardt,et al.  Theory Building From Cases: Opportunities And Challenges , 2007 .

[91]  Stefan Pauliuk,et al.  Critical appraisal of the circular economy standard BS 8001:2017 and a dashboard of quantitative system indicators for its implementation in organizations , 2018 .

[92]  F. Krausmann,et al.  How Circular is the Global Economy?: An Assessment of Material Flows, Waste Production, and Recycling in the European Union and the World in 2005 , 2015 .

[93]  Maria Antikainen,et al.  Digitalisation as an Enabler of Circular Economy , 2018 .

[94]  M. Patton,et al.  Qualitative evaluation and research methods , 1992 .

[95]  F. Blomsma,et al.  Circular economy: Preserving materials or products? Introducing the Resource States framework , 2020, Resources, Conservation and Recycling.

[96]  J. Spender,et al.  The Resource-Based View: A Review and Assessment of Its Critiques , 2009 .

[97]  Matthew B. Miles,et al.  Qualitative Data Analysis: An Expanded Sourcebook , 1994 .

[98]  F. Blomsma,et al.  The Emergence of Circular Economy: A New Framing Around Prolonging Resource Productivity , 2017 .

[99]  A. Gunasekaran,et al.  Big data analytics in logistics and supply chain management: Certain investigations for research and applications , 2016 .

[100]  Mike Wright,et al.  Strategic entrepreneurship, resource orchestration and growing spin-offs from universities , 2012, Technol. Anal. Strateg. Manag..

[101]  K. Eisenhardt Building theories from case study research , 1989, STUDI ORGANIZZATIVI.

[102]  Richard Makadok Toward a synthesis of the resource‐based and dynamic‐capability views of rent creation , 2001 .

[103]  N. Bocken,et al.  Product design and business model strategies for a circular economy , 2016 .

[104]  D. Davies,et al.  Qualitative Research and the Question of Rigor , 2002, Qualitative health research.

[105]  P. Schoemaker,et al.  Strategic assets and organizational rent , 1993 .

[106]  Kathleen M. Eisenhardt,et al.  DYNAMIC CAPABILITIES, WHAT ARE THEY? , 2000 .

[107]  Richard Vidgen,et al.  Management challenges in creating value from business analytics , 2017, Eur. J. Oper. Res..

[108]  M. Tseng,et al.  Toward Sustainability : Using Big Data to Explore Decisive Attributes of Supply Chain Risks and Uncertainties , 2017 .

[109]  Ching‐Hsun Chang How to Enhance Green Service and Green Product Innovation Performance? The Roles of Inward and Outward Capabilities , 2018 .

[110]  W. Currie,et al.  A model for unpacking big data analytics in high-frequency trading , 2017 .

[111]  Joey F. George,et al.  Toward the development of a big data analytics capability , 2016, Inf. Manag..

[112]  James G. Combs,et al.  Strategic resources and performance: a meta‐analysis , 2008 .

[113]  Anandhi S. Bharadwaj,et al.  A Resource-Based Perspective on Information Technology Capability and Firm Performance: An Empirical Investigation , 2000, MIS Q..