Peatlands drained for agriculture are among the most intensive sources of greenhouse gas (GHG) emissions from the land-use sector. Policy decisions on the most effective strategies to reduce GHG emissions in line with Paris Agreement goals, alongside strategies that can halt any ongoing soil and biodiversity losses, are hindered by a lack of understanding on how proposed mitigation measures are likely to be received by the farming sector. Research has identified effective GHG reduction measures, but successful on-farm adoption of these measures is contingent upon farmer perceptions of the relative practicality of implementing the measures, and the economic impact that adoption will have on the farm business. In this study, Best–Worst Scaling, a discrete choice survey method, was utilised to elicit expert (climate change, policy and biodiversity) and farmer opinion on the relative effectiveness, practicality and level of economic cost of mitigation measures that can reduce GHG emissions at the farm level. The method enabled individual mitigation measures to be ranked by effectiveness (expert opinion), practicality and economic cost (farmer opinions). There were no measures ranked as both effective and practical, or effective with low cost, but there were measures ranked by farmers as practical and low cost to implement. These included: more effective nutrient management, reduced or no tillage, the installation of buffer zones, increased fossil fuel efficiency and the optimisation of irrigation systems. The strong divergence of ‘effective’ measures on the one hand, and ‘practical’ and ‘economic’ measures on the other, highlights the major challenges involved in reducing high GHG emissions from agricultural organic soils. Resolving these challenges will require a combination of financial mechanisms to compensate farmers for higher costs and/or reduced yields, engagement and advice to support farmers in adopting changes in management practice, and agricultural innovation and adaptation to maintain overall food production and economic viability. If these challenges are overcome, more sustainable landscape management on agricultural lowland peat could make significant contributions to achieve national and international climate change targets.

Theories of decision-making have long been important foundations for information systems research and much of the information system is concerned with information processing for decision-making. The discipline of behavioral economics provides the dominant contemporary approach for understanding human decision-making. Therefore, it is logical that information systems research that involves decision-making should consider behavioral economics as a foundation or reference theory. Surprisingly, and despite calls for greater use of behavioral economics in information systems research, it seems that information systems has been slow to adopt contemporary behavioral economics as reference theory. This article reports a critical analysis of behavioral economics in all fields of information systems based on an intensive investigation of quality information systems research using bibliometric content analysis. The analysis shows that information systems researchers have a general understanding of behavioral economics, but their use of the theories has an ad hoc feel where only a narrow range of behavioral economic concepts and theories tend to form the foundation of information systems research. The factors constraining the adoption of behavioral economic theories in information systems are discussed and strategies for the use of this influential foundation theory are proposed. Guidance is provided on how behavioral economics could be used in various aspects of information systems. The article concludes with the view that behavioral economic reference theory has the potential to transform significant areas of information systems research.

Business intelligence and analytics (BI&A) research has been dominated by conceptual studies, simulations, and case studies, but as the field matures, experimentation is increasing. The use of students as surrogates for managers in experiments is a widespread practice in business research. Managers are difficult to recruit and organise for experimental sessions, adding to the time span and cost of a research project. This paper reports on an experiment where students and managers used a BI&A system to make simulated business decisions. The experiment is the first stage of a larger project. The study found that there were no significant differences between student and manager performance in terms of decision-making quality and efficiency, and they had similar perceptions of BI&A systems use. This paper provides preliminary evidence that high performing graduate students may be used as surrogates for managers for investigations concerning structured decisions that are supported by BI&A systems.

Decision support systems (DSS) began as a radical movement in opposition to the total management information systems (MIS) orthodoxy of the 1970s. MIS aimed to support all decisions for all managers in an organization while DSS were small-scale systems developed in an evolutionary, exploratory way to support a manager making an important decision. DSS has remained a significant part of managerial and executive work to this day. By 2020, large-scale business intelligence and analytics (BI&A) systems emerged as the major information technology (IT) expenditure in organizations—large-scale decision support had become mainstream. Using the dual process theory of decision cognition from behavioral economics as a theory lens, we analyze decision support history and identify which decisions in organizations can effectively be supported by different decision support approaches. Our analysis is at odds with IT vendors’ and consultants’ marketing narratives. We find that BI&A and data science are mainly appropriate for well-understood operational decisions, while DSS is the only approach that effectively supports strategic decision-making. We suggest that large-scale BI&A and small-scale DSS will continue to coexist into the future; the first controlled by IT departments, the second by business managers and executives.

On the 23rd June 2016, the UK referendum on European Union (EU) membership resulted in a vote to leave the EU. This departure, should it occur, would see the implementation of a new agricultural policy within the UK which will most likely see the removal of direct financial support to farmers. In this study, we use combined agricultural survey and rural payments data to evaluate the extent of reliance upon Pillar 1 payments, based on a sample of 24,492 (i.e. 70%) of farm holdings in Wales. This approach eliminates some of the variation found in the Farm Business Survey through the delivery of a more comprehensive picture on the numbers and types of farm holding potentially facing economic hardship and the quantities of land and livestock associated with those holdings. We estimate ˜34% of our sampled Welsh farm holdings face serious financial difficulties and show ˜44% of agricultural land on sampled farm holdings in Wales being vulnerable to land use change or abandonment. Based on our results, we consider the potential social and ecological impacts that the removal of direct payments may have on land use in Wales. We also discuss the use of a more balanced approach to land management that could support governmental visions to keep farmers on the land, improve productivity and deliver high quality ‘Public Goods’.

As the UK leaves the European Union, the Common Agricultural Policy (CAP), which for decades has dictated how and when farming support is delivered, will be replaced with a new UK agricultural policy which will see UK farmers, especially upland livestock farmers, facing a more challenging economic environment and a significant change to the way in which farming support is delivered. This study used a series of interviews with UK farmers across differing locations and categories to ascertain how levels of social capital may hinder or enhance a farmer's willingness to embrace future agricultural policy. We found that more conventional farmers who have never participated in agri-environment schemes and those currently in government-run schemes display high levels of bonding capital and low levels of bridging and linking capital which may hinder their ability to adapt to change. In contrast, farmers who embrace a pubic goods approach to land management displayed high bridging and linking capital and are more likely to work with government officials to adapt to policy change. Communities are more likely to become sustainable if they have access to government support and advice, and if relationships with other community members and stakeholders with an interest in rural communities, the natural environment and land management are encouraged and maintained.

This article reports on a study that is part of a larger project on how Business intelligence (BI) can effectively support a range of decisions made by different decision-makers through the lens of behavioural economics. This study examines one cognitive bias, the anchoring effect. A laboratory experiment was conducted where participants used a BI system to make a forecast. Two anchors with the same value were presented; a spurious anchor and a plausible anchor. We were interested if the use of a BI system would mitigate the negative consequences of the anchoring effect. Our results show BI system use mitigates the effect of a spurious anchor, but not a plausible anchor. That is, despite the significant expenditure on BI, decision-makers can still be subject to major biases and make less rational decisions. This study indicates that cognitive bias is an important topic for BI supported decision makingresearch and practice.

A common view of information systems (IS) researchers is that business intelligence (BI) systems are essentially a type of decision support systems (DSS). This approach to knowledge implies that DSS theory can be transferred to BI systems in order to explain and predict their action. Further, some researchers feel that BI systems can also be adequately researched using general IS theory. This paper is the first from a project that is examining if BI systems have significant differences to operational IS and DSS. This first exploration is informed by a focus group of senior BI professionals. The study illuminates some differences between BI and other types of business IS and indicates that context could be significant for BI theorizing and that care is needed in transferring operational IS and DSS theory to BI systems research. In practice, these differences could be a source of project failure.

Agri-environment schemes (AES), currently embedded in EU and UK policies, actively promote 'greening', 'sustainability' and 'ecosystem services' approaches to land management. The funding structures of these policies, however, run counter to this sustainable approach, and create barriers to AES success, primarily through a continued focus on productivity support. In this study, we aim to determine the effectiveness of action-based AES, as a delivery mechanism for ecosystem services, using secondary data analysis techniques to unravel the complexities of AES funding distribution and scheme structure and geographic information systems (GIS) to explore the spatial extent and uptake of AES management options, using Wales, UK as a study area. Our results show 84% of recipients of AES payments receiving < 10k pound annually, comprising only 35% of the total available funding. 15, out of a total of similar to 165, management options, accounted for > 75% of all advanced level management contracts awarded in both 2015 and 2017. This bias in option uptake, in many cases, positively prevents further deterioration of existing habitat condition through a 'business as usual' approach. However, we argue that the voluntary, over prescriptive nature of the schemes limits management option uptake, negatively impacts on the schemes ability to deliver ecosystem services, and lessens the government's ability to promote long-term behavioural change. If AES are to deliver the "'Public Goods"' that future policy demands, then targeted and adequate levels of funding and a willingness to participate must be combined with greater farmer autonomy and clear outcomes to deliver management options at a landscape scale.

Theories of decision-making, both prescriptive and descriptive, have long been important to decision support systems (DSS). Currently, the field of behavioral economics (BE) provides the dominant descriptive approach for understanding human decision-making. An indication of the field's standing is that three Nobel Prizes have been awarded to behavioral economics. Contemporary BE has two major theory foundations – the dual process theory of decision-making cognition and a set of judgment heuristics and cognitive biases. These foundations have been combined to create important theories like prospect theory and action strategies like nudging. Previous research has found that DSS has been slow to adopt recent advances in BE, even to the extent that some projects continue to use older theories like the phase model of decision making. This paper aims to make DSS researchers aware of contemporary BE, its nature, and its differences with early BE. We believe that behavioral economics is a useful and productive foundation for DSS research and that the use of BE in DSS should be significantly expanded.

Chinese business has developed exponentially in the last few decades and Chinese firms are highly influential in world trade. Business intelligence (BI) systems are large-scale decision support systems (DSS) that analyze enterprise data to generate business insights. BI was developed in the West and is integral to contemporary Western management practices. It is generally assumed that western BI systems are useable and effective in a Chinese context. No study has been undertaken to investigate the use behavior of large-scale DSS in Chinese organizations. We conducted two exploratory case studies in large indigenous Chinese organizations. The case analysis shows that a complex cultural factor (provisionally termed Factor X) affects BI systems use in China. A set of propositions are formulated from the analysis. They will be used as a foundation for future research on Chinese BI.

Business intelligence (BI) is often used as the umbrella term for large-scale decision support systems (DSS) in organizations. BI is currently the largest area of IT investment in organizations and has been rated as the top technology priority by CIOs worldwide for many years. The most important use patterns in decision support are concerned with the type of decision to be supported and the type of manager that makes the decision. The seminal Gorry and Scott Morton MIS/DSS framework remains the most popular framework to describe these use patterns. It is widely believed that DSS theory like this framework can be transferred to BI. This paper investigates BI systems use patterns using the Gorry and Scott Morton framework and contemporary decision-making theory from behavioral economics. The paper presents secondary case study research that analyzes eight BI systems and 86 decisions supported by these systems. Based on the results of the case studies a framework to describe BI use patterns is developed. The framework provides both a theoretical and empirically based foundation for the development of high quality BI theory. It also provides a guide for developing organizational strategy for BI provision. The framework shows that enterprise and smaller functional BI systems exist together in an organization to support different decisions and different decision makers. The framework shows that personal DSS theory cannot be applied to BI systems without specific empirical support.

Providing active, interventionist support is advocated as a means of decision support that goes beyond the traditional passive approaches. A differential study conducted as the first stage of this project showed that senior decision-makers with different personality dispositions display distinct preferences when making decisions. This paper presents a decision support architecture that attempts to provide the basis for systems that are capable of adapting to their users based on their personality preferences. An implementation of the architecture is also discussed. Repeated use of a decision support system is expected to give the system the knowledge required to incrementally adapt to the decision preferences of the individual. The capabilities of adaptation are facilitated through the construction of profiles of decision-makers, decision situations and decision domains. The profiles are maintained as criteria preference models. An inference mechanism that utilises neural networks is used to build these profiles. The approximations provided by the system are expected to improve progressively. A mature system may be capable of identifying inconsistencies in an individual's decision-making and provide active decision support. Presented at: 4th Conference of the International Society for Decision Support Systems; 1997 Jul 21-22; Lausanne, Switzerland. 18 leaves

Studio-based teaching focuses on problem/project work and experimentation in a hands-on studio environment. Traditionally found in the arts and architecture, it is now being used in other fields such as Instructional Technology and the sciences. The first link below describes the history and rationale of studio-based teaching. Subsequent links provide information on studio teaching in a variety of fields.

If different individuals can be shown to behave differently in the presence of the same decision situation, then it is likely that there is a link between the unique personality of an individual and the decisions that that person make. Many studies have attempted to manifest individual factors that may affect decision making. If the presence of such factors can be theoretically validated, they can be used as a basis of DSS design. These systems can be expected to provide support that is compatible with the needs of the targeted decision maker. To enable such a systems development paradigm, the effect of specific personality constructs and the methods of extracting those constructs should be articulated. This paper reviews contemporary personality and personality assessment literature and attempts to evaluate whether we currently have the necessary knowledge to undertake the development of decision support systems which can genuinely adapt to the user. In: Proceedings of the 3rd International Conference on Decision Support Systems; 1995 Jun 22-23; Hong Kong [place unknown]: [publisher unknown]; 1995. p. 201-207.

This paper critically analyses the nature and state of decision support systems (DSS) research. To provide context for the analysis, a history of DSS is presented which focuses on the evolution of a number of sub-groupings of research and practice: personal DSS, group support systems, negotiation support systems, intelligent DSS, knowledge management-based DSS, executive information systems/business intelligence, and data warehousing. To understand the state of DSS research an empirical investigation of published DSS research is presented. This investigation is based on the detailed analysis of 1,020 DSS articles published in 14 major journals from 1990 to 2003. The analysis found that DSS publication has been falling steadily since its peak in 1994 and the current publication rate is at early 1990s levels. Other findings include that personal DSS and group support systems dominate research activity and data warehousing is the least published type of DSS. The journal DSS is the major publishing outlet; US ‘Other’ journals dominate DSS publishing and there is very low exposure of DSS in European journals. Around two-thirds of DSS research is empirical, a much higher proportion than general IS research. DSS empirical research is overwhelming positivist, and is more dominated by positivism than IS research in general. Design science is a major DSS research category. The decision support focus of the sample shows a well-balanced mix of development, technology, process, and outcome studies. Almost half of DSS papers did not use judgement and decision-making reference research in the design and analysis of their projects and most cited reference works are relatively old. A major omission in DSS scholarship is the poor identification of the clients and users of the various DSS applications that are the focus of investigation. The analysis of the professional or practical contribution of DSS research shows a field that is facing a crisis of relevance. Using the history and empirical study as a foundation, a number of strategies for improving DSS research are suggested.

Despite extensive research into the evaluation of the success of decision support systems (DSS), criteria important in measuring success are rarely defined. This may be critical when a number of reference groups, or constituencies, are considered. In multiple-constituency DSS evaluation the understanding of each criteria may be different for each constituency. Also, each constituency will have distinct requirements of the DSS and will consider a different set of criteria to measure these needs. This paper defines criteria for use within a multiple-constituency evaluation process and determines appropriate hierarchies of criteria for such a process. This forms the basis for ongoing research into multiple-constituency DSS evaluation.Presented at: 6th Australasian Conference on Information Systems; 1995 Sep 26-29; Perth, Australia. p. 699-713.

In 2005 the Journal of Information Technology article ‘A critical analysis of decision support systems research’ analyzed 1020 decision support systems (DSS) articles from 1990 to 2003. Since 2003 business intelligence (BI) and business analytics have gained popularity in practice. In theory and research the period since 2003 has seen a change in the decision-making theory orthodoxy and the codification and acceptance of design science. To investigate the changes in the DSS field, a number of expectations were derived from previous literature analyses. These expectations were assessed using bibliometric content analysis. The article sample to 2010 now includes 1466 articles from 16 journals. The analysis of the expectations yields mixed results for the DSS field. On the negative side, there has been an overall decline in DSS publishing, the relevance of DSS research published in journals to IT professionals has declined, and the rigor of DSS research designs has not improved. On the positive side, there has been improvement in relevance to managers, grant funding of DSS research has increased, there has been a positive shift in judgment and decision-making foundations, BI publishing has increased, and group support systems publishing has reduced to a more balanced level. An important result from the analysis of the last 7 years of DSS research is the significant increase in DSS design-science research (DSR) to almost half of published articles. It is clear from the analysis that DSS is undergoing a transition from a field based on statistical hypothesis testing and conceptual studies to one where DSR is the most popular method.