Bioenergy Decision Support Systems: Worth the Effort? Daniel Wright , Prasanta Dey, John Brammer & Phil Hunt Email: wrightd1@aston.ac.uk ESRC CASE Studentship Project
Agenda To explore the disparity between the existing model-orientated bioenergy DSS and what is desired by the practitioner Format Introduction Research Objectives DSS Key Issues Bioenergy Literature Methodology Results and Analysis Conclusion
Introduction Developing a bioenergy project requires large volumes of complex information to be gathered and processed by developers The information tends to be fairly structured and accessible; although, often not easily retrievable in a timely manner (Mitchell, 2000) Presently, there is little evidence that these DSS are applied in practice
Research Objectives Ascertain whether bioenergy DSS are worth the effort, by: a) Reviewing published bioenergy project development DSS models b) Critically comparing these models to the requirements of the industry practitioner c) Discussing how future DSS could be more applicable to the practitioner
The (not so) White Elephant “… [DSS] often fail to be fully taken up in practice “Decision support systems couple the because the designers and intellectual resources of individuals modellers have not worked with the capabilities of the computer fully in concert with users of the product” (Mitchell, 2000) to improve the quality of decisions. It is a computer-based support system for management decision makers who deal with semistructured problems” Theory-practice (Keen and Scott-Morton, 1978) divide “The issue of bridging the gap is a “… IS researchers have much more complex one and the lamented the supposed poor entire DSS community should pay state of the relationship more attention to redirect our between IS research and research efforts” (Eom, 2007) practice for many years” (Baskerville & Myers, 2009)
DSS Key Issues Key Issue Comments Professional Most DSS research is disconnected from practice. relevance Research DSS is more dominated by positivism than general IS [information methods and systems]. Case study research is under represented. A long history of paradigms design science research could contribute methodologically to IS research. Around half of the papers have no explicit foundation in judgement and Theoretical decision-making. Much DSS research is based on a relatively old foundations theoretical foundation. Inertia and The relatively older types of PDSS and GSS still dominate research conservatism agendas. Conducted a content analysis of 1093 DSS articles published in 14 major journals form 1990 to 2004 Identified eight key issues of the DSS discipline (adapted from Arnott and Pervan, 2008)
Bioenergy DSS Timeline Annotated timeline of model-oriented bioenergy DSS research. 13 model-orientated DSS papers for bioenergy reviewed The complexity of decision making in this emerging industry Mostly developed and published in the past decade
Methodology Exploratory insight Apply a similar content review to Arnott and Pervan’s (2008) study Compare this to a practitioner’s perspective through an interview and closed question, Likert-scale questionnaire Practitioner Background Managing Director SME developer and operator of small-scale biomass CHP schemes in the UK
Methodology: Characteristics Characteristic Classification Type of DSS Personal DSS (PDSS) Group DSS (GSS) Knowledge-based Knowledge management based User(s) National or regional developer Local developer Investor Implied/not-stated Method Empirical Non-empirical Low/medium (single application) Practical relevance High/very high (multiple applications) Yes Theoretical foundation No Planning Bioenergy lifecycle phase Construction Operation Financial Model output Non-financial Both
Importance Weighting Scale Importance Journals Practical Relevance Measure Low 0-2 Hypothetical case Medium 3-5 Single application or case study High 6-8 Multiple practical uses Very high 9+ Multiple practical uses and application examples Required a method for comparing the academic and practitioners' weighting of importance Practical relevance construct also needed adapting Arnott and Pervan (2008) found that when cross-tabulating research type and practical relevance, that case studies had the highest proportion of high or very high relevance (35.9%)
Results and Analysis DSS User Type of DSS 10 V. high High 3 Medium 2 2 10 0 0 0 Low Knowledge-based Knowledge mgmt’ PDSS GSS National or Local Investor Implied/not stated regional based Academic Weighting (no.) Practitioner Weighting Academic Weighting (no.) Practitioner Weighting The literature heavily supports PDSS and tends to not explicitly state the intended user of the support tool; Whereas, the practitioner emphasises the importance of a wider range of DSS types (except the knowledge mgmt. type) and strongly believes that all users need targeting
Results and Analysis Method Applied Theoretical Foundation 11 V. high 7 6 High Medium 2 Low N/A Yes No Empirical Non-empirical Academic Weighting (no.) Practitioner Weighting Academic Weighting (no.) Practitioner Weighting The academic literature was split across empirical and non-empirical studies, they also tended to lack a theoretical foundation under Arnott and Pervan’s classification The practitioner saw the merits of non-empirical, but emphasised the importance of empirical and a strong theoretical foundation
Results and Analysis Lifecycle Phase Model Output 13 V. high 7 High 4 Medium 0 0 2 Low N/A Planning Construction Operation Financial Non-financial Both Academic Weighting (no.) Practitioner Weighting Academic Weighting (no.) Practitioner Weighting The existing DSS were aimed at only the planning phase of the project lifecycle; Whereas, the practitioner placed a very high importance on all phases of the lifecycle, and the highest weighting on the financial output
Results and Analysis Practical Relevance 10 V. high High 3 Medium Low Low/med (single application) High/v.high (multiple applications) Academic Weighting (no.) Practitioner Weighting The majority of academic papers possessed a low to medium level of practical relevance (hypothetical or single case study) The practitioner thought that a single case applicable DSS would be useful, but more greatly valued a generalisable model
Conclusions The lack of a theoretical foundation in the majority of bioenergy DSS literature, implied DSS users and low/medium practical relevance are the most significant findings Requires better collaboration and understanding of the user requirements Management buy-in would increase model adoption Limitations and Further Research Small literature sample size (13 papers) Increase sample size as part of a further enquiry Targeting national developers and investors in bioenergy projects would reduce practitioner type bias
Thank you for listening
References Arnott, D. and G. Pervan (2008). "Eight key issues for the decision support systems discipline." Decision Support Systems 44 (3): 657-672. Baskerville, R. L. and M. D. Myers (2009). "Fashion Waves in Information Systems: Research and Practice." MIS Quarterly 33 (4): 647-662. Benbasat, I. and R. W. Zmud (1999). "Empirical research in information systems: the practice of relevance." MIS Q. 23 (1): 3-16. Eom, S. B. (2007). The Development of Decision Support Systems Research. New York, The Edwin Mellen Press Ltd. Hirschheim, R. A. and H. K. Klein (2003). "Crisis in the IS field? A critical reflection on the state of the discipline." Journal of the Association for Information Systems 4 (1): 237-293. Keen, P. G. W. and S. S. Scott-Morton (1978). Decision Support Systems: An Organizational Perspective. Harlow, Pearson Education Limited. Mitchell, C. P. (2000). "Development of decision support systems for bioenergy applications." Biomass and Bioenergy 18 (4): 265-278.
Recommend
More recommend
