Bioenergy Decision Support Systems: Worth the Effort? Daniel Wright - - PowerPoint PPT Presentation

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

Theory-practice divide “…[DSS] often fail to be fully taken up in practice because the designers and modellers have not worked fully in concert with users of the product” (Mitchell, 2000) “The issue of bridging the gap is a much more complex one and the entire DSS community should pay more attention to redirect our research efforts” (Eom, 2007) “…IS researchers have lamented the supposed poor state of the relationship between IS research and practice for many years” (Baskerville & Myers, 2009) “Decision support systems couple the intellectual resources of individuals with the capabilities of the computer to improve the quality of decisions. It is a computer-based support system for management decision makers who deal with semistructured problems” (Keen and Scott-Morton, 1978)

DSS Key Issues

Key Issue Comments Professional relevance Most DSS research is disconnected from practice. Research methods and paradigms DSS is more dominated by positivism than general IS [information systems]. Case study research is under represented. A long history of design science research could contribute methodologically to IS research. Theoretical foundations Around half of the papers have no explicit foundation in judgement and decision-making. Much DSS research is based on a relatively old theoretical foundation. Inertia and conservatism The relatively older types of PDSS and GSS still dominate research 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

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

Annotated timeline of model-oriented bioenergy DSS research.

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

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 Practical relevance Low/medium (single application) High/very high (multiple applications) Theoretical foundation Yes No Bioenergy lifecycle phase Planning Construction Operation Model output Financial Non-financial Both

Methodology: Characteristics

Importance Weighting Scale

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%)

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

Results and Analysis

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

• V. high

High Medium Low

PDSS GSS Knowledge-based Knowledge mgmt’ based

Type of DSS

Academic Weighting (no.) Practitioner Weighting

10 3

National or regional Local Investor Implied/not stated

DSS User

Academic Weighting (no.) Practitioner Weighting

2 2 10

Yes No

Theoretical Foundation

Academic Weighting (no.) Practitioner Weighting

Results and Analysis

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

Empirical Non-empirical

Method Applied

Academic Weighting (no.) Practitioner Weighting

7 6 2 11 N/A

• V. high

High Medium Low

Planning Construction Operation

Lifecycle Phase

Academic Weighting (no.) Practitioner Weighting

Results and Analysis

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

• V. high

High Medium Low

Financial Non-financial Both

Model Output

Academic Weighting (no.) Practitioner Weighting

N/A 2 7 13 4

Low/med (single application) High/v.high (multiple applications)

Practical Relevance

Academic Weighting (no.) Practitioner Weighting

Results and Analysis

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

3 10

• V. high

High Medium Low

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

• f 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.