Use Of Decision Support Systems Research Articles

Typology for Decision Support Systems in Integrated Pest Management and Its Implementation as a Web Application

Decision support systems (DSSs) enable the optimisation of pesticide application timing to increase pesticide efficacy and thus reduce pesticide use without compromising yield quality and quantity. Limited access to information about available DSSs for use in integrated pest management (IPM) is a major barrier to the uptake of DSSs for IPM across Europe. To overcome this barrier, a typology for DSSs for IPM in Europe was developed, introducing a systematic approach to describe the ever-growing number of DSSs for IPM. The developed IPM-DSS typology was implemented in the free web tool “IPM Adviser”, where currently 79 IPM DSSs are described with over 50 attributes describing their structural and performance characteristics. The information about IPM DSSs, which was previously scattered on different websites and difficult to compare, is now standardised and presented in a uniform way, so that it is possible to compare different IPM DSSs on the basis of all the attributes described. The presented IPM-DSS typology implemented in the web tool IPM Adviser facilitates the dissemination and uptake of DSSs for IPM and thus contributes to the achievement of the EU targets for the sustainable use of pesticides.

Review of climate change and drinking water supply systems: employee perspectives and potential tools for adaptation

Climate change (CC) causes extreme meteorological events such as floods, droughts, water scarcity, heat waves, wildfires, and extreme cold. CC may also damage public infrastructures such as drinking water systems (DWSs) and reduce water quality from source to tap. The objective of this review paper is to provide a critical analysis of (1) the impact of CC on the three components of the DWSs: source, treatment, and distribution; (2) the perspectives of DWS employees on CC impacts and adaptation solutions for maintaining water quality; and (3) decision support systems (DSSs) that could be used by DWS employees for CC adaptation. A bibliographic research was conducted using international databases and search engines. The search for scientific articles resulted in the selection of 5234 articles, 78 of which were analyzed in more detail for the elaboration of this scoping review. This review shows that the impacts of CC on water quality are significant, but DWS employees are not enthusiastic about developing adaption measures. This review also shows that DSSs can help DWS employees facilitate the CC adaptation process for the protection of source water, improve water treatment plant operations, and enhance routine management of water quality in the distribution system. Further research is needed to identify strategies that might increase the awareness of and interest in climate change impacts, adaptation, and the use of DSSs among DWS employees.

3BResNet: COVID19 Tespiti için Yeni Bir Artık Blok Tabanlı ResNet Modeli Yaklaşımı

In recent years, upper respiratory tract infections that have affected the whole world have caused the death of millions of people. It is predicted that similar infections may occur in the coming years. Therefore, it is necessary to develop methods that can be used widely, especially during epidemic periods. The study developed a decision support system for use in upper respiratory tract infections. At this stage, first, the ResNet models in the literature were examined and an application was developed on the SARS-CoV-2 Ct dataset. Next stage, the block structure in the ResNet models in the literature was changed, the number of layers was reduced, and a new model was proposed that provides higher success with fewer parameters. With the proposed model, the values 0.97, 0.97, 0.94, and 0.98 were achieved for accuracy, F1 score, precision and sensitivity on the SARS-CoV-2 Ct dataset, respectively. When the obtained values are compared to state of the art methods in the literature, it has been determined that they are at a competitive level with much fewer parameters. Hardware-related problems encountered in the training of ResNet models at low hardware levels were solved with the proposed model, resulting in a higher success rate. Furthermore, the proposed model can be widely used in different decision support systems that are urgently needed in adverse conditions such as pandemics due to its lightweight structure and high-performance results.

Decision Support Systems (DSSs) ‘In the Wild’: The Factors That Influence Users’ Acceptance of DSSs in Naturalistic Settings

A richer approach to studying Decision Support System (DSS) interactions is required to understand and predict the nature of actual use in the workplace. We used questionnaire and interview techniques to examine workers’ experiences relating to DSS use in naturalistic settings. We aimed to: 1) Reveal what workers perceive to be the most important factors when deciding whether to accept support from a DSS and 2) Elicit patterns that emerge from DSS users’ recounted experiences using the systems, which may impact their future use. Current and prospective DSS users ( N = 93) from numerous industries responded to a questionnaire relating to the factors they perceive to influence their use of DSSs. Subsequently, a retrospective interview protocol was employed to investigate the experiences of a subset of DSS users ( N = 10). The questionnaire results underscore a range of factors considered to be very important to the acceptance of DSSs (i.e. decision quality; decision importance; decision risk; historical accuracy; decision accountability; and system comprehension). Further, a series of interconnected themes relating to workers’ use of DSSs were identified from the interview transcripts using thematic analysis. We discuss how these issues may impact workers’ intentions to use DSSs in the workplace, and advocate for the use of naturalistic decision-making techniques to study technology acceptance.

Can Decision Support Systems Help Improve the Sustainable Use of Fungicides in Wheat?

Wheat is one of the most economically important field crops worldwide. Foliar diseases are a major threat to wheat productivity and are primarily managed by implementing less susceptible cultivars and using fungicides. With the “Farm to Fork” strategy under consideration by the European Union to reduce pesticide usage by 50% by 2030, this elucidates the importance of utilizing decision support systems (DSS) to optimize fungicide applications. Therefore, three DSSs of different origins, namely the IPM-Wheat Model Schleswig-Holstein (scientific), the ISIP system (federal), and the xarvio© FIELD MANAGER (commercial), were analysed under maritime climate conditions at three locations in a high input area of wheat cultivation in northern Germany from 2019 to 2021. Fungicide efficacy was evaluated for yield as well as for the management of prevalent pathogens (Septoria tritici blotch, glume blotch, tan spot, powdery mildew, stripe rust, and leaf rust) on two different commercially available cultivars (highly and moderately susceptible). Compared to a stage-oriented standard system, no significant decrease in yield was observed in both cultivars, despite up to a 50% reduction in fungicide use through the use of DSSs. This was attributed to an optimized timing of fungicide applications, which resulted in slightly lower but still tolerable disease suppression efficacy compared to the stage-oriented system. In conclusion, minor disease severities are often overestimated, and DSSs can help improve the sustainability of fungicide use in wheat and pesticides in general.

Designing and Evaluating a Personalized, Human-Centered Dietary Decision Support System for Use Among People With Diabetes in an Indian Setting: Protocol for a Quasi-Experimental Study

BackgroundHuman-centered dietary decision support systems are fundamental to diabetes management, and they address the limitations of existing diet management systems.ObjectiveThe objective of the proposed study is to evaluate the use of an interactive, telephone-linked, personalized, human-centered decision support system for facilitating the delivery of personalized nutrition care for patients with diabetes.MethodsA quasi-experimental trial was conducted between the period of June and December 2018. Study participants were recruited from Community Health Center, Dharamshala, Kangra (urban population), and Model Rural Health Unit, Haroli Block, Una (rural population). Eligible participants included adults aged ≥30 years with controlled or uncontrolled diabetes, those who agreed to participate in the study, those who were available for follow-up interviews, and those with a telephone or computer at home. Diabetic status was determined via a physician’s diagnosis. Individuals with mental or physical challenges that affected their ability to use an electronic diet record, those who were not available for a telephone follow-up, and those who were involved in other protocols related to dietary assessments were excluded. The study participants were randomized into the following two groups: the intervention group (telephone-linked dietary decision support system) and the control group (paper-based diet record). Study participants in the intervention group recorded their daily dietary intake by using a telephone-linked, personalized, human-centered dietary decision support system and received personalized feedback and diet education via SMS text messaging. Study participants in the control group were provided with only a paper-based diet record for documenting their daily dietary intake. Follow-up visits were conducted at 3 and 6 months from the baseline in both groups. Differences in diabetes knowledge, attitudes, and practices will be measured across groups.ResultsThe collection of baseline data from 800 study participants in both the intervention (n=400) and control groups (n=400), which were stratified by urban (control group: n=200; intervention group: n=200) and rural settings (control group: n=200; intervention group: n=200), has been completed. Follow-up data collection for months 3 and 6 is ongoing and is expected to be completed by October 2019.ConclusionsWe anticipate that the intervention group will show significant changes in nutrition knowledge, attitudes, and practices; satisfaction with care; and overall diabetes management. We also expect to see urban-rural differences across the groups. The uniqueness of our nutrient data capture process is demonstrated by its cultural and contextually relevant features—diet capture in both English and Hindi, diet conversion into caloric components, sustained diet data collection and participant adherence through telephone-linked care, and auto-generated reminders.International Registered Report Identifier (IRRID)DERR1-10.2196/13635

The effect of decision support systems on pain in patients with cancer: A systematic review and meta‐analysis of randomized controlled trials

This study was conducted to systematically examine the effect of decision support systems (DSSs) applied to patients with cancer on pain severity. Systematic review and meta-analysis. A search was done on Web of Science, Science Direct, PubMed, ProQuest, EBSCOhost/CINAHL Complete, Scopus, Springer Link, Cochrane Library, and Ovid databases, which covered a period until September 2021. Meta-analysis of the data was conducted on the CMA 3 software package. Comprehensive reviews were conducted by two independent researchers in line with the PICOS criteria. The study was conducted according to the PRISMA checklist. Five randomized controlled trials with 1.880 participants were included in this systematic review and meta-analysis. In the study, visits, consultations, simulation of patient outcomes, telephone support, and email applications were employed for periods ranging from 6weeks to 6months. The evaluation of the meta-analysis results indicated that DSSs had positive effects on pain severity in patients with cancer (Hedge's g=0.22; p<0.001). The findings of this systematic review and meta-analysis show that DSSs can be used as an effective and comfortable technological application in reducing the severity of pain in patients with cancer. The use of DSSs for pain severity in patients with cancer is an effective method. In line with the findings of this systematic review and meta-analysis, awareness and knowledge levels of all health disciplines about DSSs will increase. It is believed that the use of DSSs to improve patient-centered care will be guiding.

A decision support system for automating document retrieval and citation screening

The systematic literature review (SLR) process includes several steps to collect secondary data and analyze it to answer research questions. In this context, the document retrieval and primary study selection steps are heavily intertwined and known for their repetitiveness, high human workload, and difficulty identifying all relevant literature. This study aims to reduce human workload and error of the document retrieval and primary study selection processes using a decision support system (DSS). An open-source DSS is proposed that supports the document retrieval step, dataset preprocessing, and citation classification. The DSS is domain-independent, as it has proven to carefully select an article’s relevance based solely on the title and abstract. These features can be consistently retrieved from scientific database APIs. Additionally, the DSS is designed to run in the cloud without any required programming knowledge for reviewers. A Multi-Channel CNN architecture is implemented to support the citation screening process. With the provided DSS, reviewers can fill in their search strategy and manually label only a subset of the citations. The remaining unlabeled citations are automatically classified and sorted based on probability. It was shown that for four out of five review datasets, the DSS's use achieved significant workload savings of at least 10%. The cross-validation results show that the system provides consistent results up to 88.3% of work saved during citation screening. In two cases, our model yielded a better performance over the benchmark review datasets. As such, the proposed approach can assist the development of systematic literature reviews independent of the domain. The proposed DSS is effective and can substantially decrease the document retrieval and citation screening steps' workload and error rate.

The Human Dimensions of Spatial, Pre-Wildfire Planning Decision Support Systems: A Review of Barriers, Facilitators, and Recommendations

Decision support systems (DSSs) are increasingly common in forest and wildfire planning and management in the United States. Recent policy direction and frameworks call for collaborative assessment of wildfire risk to inform fuels treatment prioritization using the best available science. There are numerous DSSs applicable to forest and wildfire planning, which can support timely and relevant information for decision making, but the use and adoption of these systems is inconsistent. There is a need to elucidate the use of DSSs, specifically those that support pre-wildfire, spatial planning, such as wildfire risk assessment and forest fuels treatment prioritization. It is important to understand what DSSs are in use, barriers and facilitators to their use, and recommendations for improving their use. Semi-structured interviews with key informants were used to assess these questions. Respondents identified numerous barriers, as well as recommendations for improving DSS development and integration, specifically with respect to capacity, communication, implementation, question identification, testing, education and training, and policy, guidance, and authorities. These recommendations can inform DSS use for wildfire risk assessment and treatment prioritization to meet the goals of national policies and frameworks. Lastly, a framework for organizing spatial, pre-wildfire planning DSSs to support end-user understanding and use is provided.

Detection of mitotic cells in breast cancer histopathological images using deep versus handcrafted features

One of the most important processes in the diagnosis of breast cancer, which is the leading mortality rate in women, is the detection of the mitosis stage at the cellular level. In literature, many studies have been proposed on the computer-aided diagnosis (CAD) system for detecting mitotic cells in breast cancer histopathological images. In this study, comparative evaluation of conventional and deep learning based feature extraction methods for automatic detection of mitosis in histopathological images are focused. While various handcrafted features are extracted with textural/spatial, statistical and shape-based methods in conventional approach, the convolutional neural network structure proposed on the deep learning approach aims to create an architecture that extracts the features of small cellular structures such as mitotic cells. Mitosis detection/counting is an important process that helps us assess how aggressive or malignant the cancer’s spread is. In the proposed study, approximately 180,000 non-mitotic and 748 mitotic cells are extracted for the evaluations. It is obvious that the classification stage cannot be performed properly due to the imbalanced numbers of mitotic and non-mitotic cells extracted from histopathological images. Hence, the random under-sampling boosting (RUSBoost) method is exploited to overcome this problem. The proposed framework is tested on mitosis detection in breast cancer histopathological images dataset provided from the International Conference on Pattern Recognition (ICPR) 2014 contest. In the results obtained with the deep learning approach, 79.42% recall, 96.78% precision and 86.97% F-measure values are achieved more successfully than handcrafted methods. A client/server-based framework has also been developed as a secondary decision support system for use by pathologists in hospitals. Thus, it is aimed that pathologists will be able to detect mitotic cells in various histopathological images more easily through necessary interfaces.

Benefits and Limitations of Decision Support Systems (DSS) with a Special Emphasis on Weeds

Decision support systems (DSS) have the potential to support farmers to make the right decisions in weed management. DSSs can select the appropriate herbicides for a given field and suggest the minimum dose rates for an herbicide application that can result in optimum weed control. Given that the adoption of DSSs may lead to decreased herbicide inputs in crop production, their potential for creating eco-friendly and profitable weed management strategies is obvious and desirable for the re-designing of farming systems on a more sustainable basis. Nevertheless, it is difficult to stimulate farmers to use DSSs as it has been noticed that farmers have different expectations of decision-making tools depending on their farming styles and usual practices. The function of DSSs requires accurate assessments of weeds within a field as input data; however, capturing the data can be problematic. The development of future DSSs should target to enhance weed management tactics which are less reliant on herbicides. DSSs should also provide information regarding weed seedbank dynamics in the soil in order to suggest management options not only within a single period but also in a rotational view. More aspects ought to be taken into account and further research is needed in order to optimize the practical use of DSSs for supporting farmers regarding weed management issues in various crops and under various soil and climatic conditions.

Time for clinical decision support systems tailoring individual patient therapy to improve renal and cardiovascular outcomes in diabetes and nephropathy.

The current guideline treatment for patients with diabetes and nephropathy to lower the high risk of renal and cardiovascular (CV) morbidity and mortality is based on results of clinical studies that have tested new drugs in large groups of patients with diabetes and high renal/CV risk. Although this has delivered breakthrough therapies like angiotensin receptor blockers, the residual renal/CV risk remains extremely high. Many subsequent trials have tried to further reduce this residual renal/CV risk, without much success. Post hoc analyses have indicated that these failures are, at least partly, due to a large variability in response between and within the patients. The current ‘group approach’ to designing and evaluating new drugs, as well as group-oriented drug registration and guideline recommendations, does not take this individual response variation into account. Like with antibiotics and cancer treatment, a more individual approach is warranted to effectively optimize individual results. New tools to better evaluate the individual risk change have been developed for improved clinical trial design and to avoid trial failures. One of these tools, the composite multiple parameter response efficacy score , is based on monitoring changes in all available risk factors and integrating them into a prediction of ultimate renal and CV risk reduction. This score has also been modelled into a clinical decision support system for use in monitoring and changing the therapy in individual patients to protect them from renal/CV events. In conclusion, future treatment of renal/CV risk in diabetes should transition from an era of ‘one size fits all’ into the new era of ‘a fit for each size’.

An Ontology-Based Personalized Decision Support System for Use in the Complex Chronically Ill Patient.

Management of the complex chronically ill patient is highly problematic. This is due to the need to complement recommendations in clinical guidelines with recommendations based on treatments performed on a representative set of patients. It is necessary to identify conflicts between the recommendations of different guidelines designed for handling specific chronic diseases. The PITeS-TIiSS project (code PI15/01213) aims to overcome this problem by designing and deploying an ontology-based personalized clinical decision support tool. This helps to improve personalized decisions and reduces variability in clinical practice in an integrated care environment. This paper describes the methodology followed in developing the ontology used to infer clinical knowledge and to develop and implement the support tool. The tool will access the information provided by the Electronic Health Record of Andalusian Health Public Service, the main healthcare provider in a region in Spain with more than eight million inhabitants.

Introducing a group spatial decision support system for use in strategic environmental assessment of onshore wind farm development in Mexico

In this paper we introduce a method for potential use in strategic environmental assessment (SEA) which integrates knowledge and tools of Collective Intelligence, Complexity Theory and Geoprospective, via the implementation of a technological Group-Spatial Decision Support System (GSDSS), usable for scenario building for infrastructure project planning. It operates on the basis of interdisciplinary consensus of a multidisciplinary group of experts, without strict dependency on a spatial analysis based on a single cognitive stance, using existing historical data. The method is used in a study case on planning of onshore wind energy in Mexico, which has been developed through a collaborative Geoweb application, and is functioning in a distributed and asynchronous real-time way, so-called Geospatial System of Collective Intelligence (SIGIC). The significance of this research lies in demonstrating the feasibility of a collective intelligence analysis aimed at proposing spatial locations in an energy transition context within an SEA framework.

An Electronic Clinical Decision Support System for the Assessment and Management of Suicidality in Primary Care: Protocol for a Mixed-Methods Study.

BackgroundSuicide is a global public health concern, but it is preventable. Increased contact with primary care before the suicide or attempted suicide raises opportunities for intervention and prevention. However, suicide assessment and management are areas that many general practitioners (GPs) find particularly challenging. Previous research has indicated significant variability in how GPs understand, operationalize, and assess suicide risk, which subsequently has an impact on clinical decision making. Clinical decision support systems (CDSS) have been widely implemented across different health care settings, including primary care to support practitioners in clinical decision making. A CDSS may reduce inconsistencies in the identification, assessment, and management of suicide risk by GPs by guiding them through the consultation and generating a risk assessment plan that can be shared with a service user or with specialized mental health services.ObjectiveOur aim is to co-develop and test with end users (eg, GPs, primary care attendees, mental health professionals) an electronic clinical decision support system (e-CDSS) to support GPs in the identification, assessment, and management of suicidality in primary care.MethodsOurs is an ongoing embedded mixed-methods study with four phases: (1) qualitative interviews with GPs to explore their views on the content, format, and use of the e-CDSS, as well as consultation with two service-user advisory groups (people aged ≤25 and people aged ≥25) to inform the content of the e-CDSS including phrasing of items and clarity; (2) participatory co-production workshops with GPs, service users, and clinical experts in suicidality to determine the content and format of the e-CDDS; gain consensus of the relevance of items; establish content validity and identify pathways to implementation, using the Consolidated Framework for Implementation Research; (3) building the e-CDSS so that it guides the GP through a consultation; and (4) usability testing of the e-CDSS with GPs and service users in one primary care practice involving a nonlive and a live stage.ResultsThe study was funded for four years, to take place between 2015 and 2019, and is currently completing phase 4 data collection. The first results are expected to be submitted for publication in June 2019. The findings will enable us to evaluate the feasibility, acceptability, and usability of a suicide-specific, electronic, guided decision support system in primary care.ConclusionsThis study will be the first to explore the feasibility, acceptability, and usability of an electronic, guided decision support system for use in primary care consultations for the improved assessment and management of suicidality.International Registered Report Identifier (IRRID)RR1-10.2196/11135

The Development of a Novel Decision Support System for the Location of Green Infrastructure for Stormwater Management

In order to maximise the benefits of green infrastructure in a city’s structure for urban adaptation to climate change, there is a need to support decision-makers in the urban design domain with adequate information that would help them to locate such green infrastructure in the most suitable places. Therefore, the aim of this study was to develop a novel decision support system (DSS) for the location of green infrastructure. The goal of the designed solution is to inform users about the location of urban hydrological sinks, which gather stormwater in urban watersheds, and the amount of water which could accumulate in each location depending on the defined precipitation and the soil’s moisture conditions. The designed DSS is based on a multicomponent methodology including both atmospheric and soil conditions. The DSS was tested using a sample that presents the results of stormwater accumulation calculations. The obtained results show which green areas are the most suitable locations for green infrastructure solutions and which facility is optimal because of its retention abilities and amount of accumulated stormwater. The application of the designed DSS allows us to maximise the benefits of the implementation of green infrastructure within the existing urban land use. The fully editable component of hydrological conditions allows for testing projections of the potential amount of accumulated water in different precipitation scenarios. The study provides a DSS for use by local authorities which enables them to concentrate actions in order to better adapt cities to climate change and environmental extremes.

Digital Technologies for Forest Supply Chain Optimization: Existing Solutions and Future Trends

The role of digital technologies for fostering sustainability and efficiency in forest-based supply chains is well acknowledged and motivated several studies in the scope of precision forestry. Sensor technologies can collect relevant data in forest-based supply chains, comprising all activities from within forests and the production of the woody raw material to its transformation into marketable forest-based products. Advanced planning systems can help to support decisions of the various entities in the supply chain, e.g., forest owners, harvest companies, haulage companies, and forest product processing industry. Such tools can help to deal with the complex interdependencies between different entities, often with opposing objectives and actions-which may increase efficiency of forest-based supply chains. This paper analyzes contemporary literature dealing with digital technologies in forest-based supply chains and summarizes the state-of-the-art digital technologies for real-time data collection on forests, product flows, and forest operations, as well as planning systems and other decision support systems in use by supply chain actors. Higher sustainability and efficiency of forest-based supply chains require a seamless information flow to foster integrated planning of the activities over the supply chain-thereby facilitating seamless data exchange between the supply chain entities and foster new forms of collaboration. Therefore, this paper deals with data exchange and multi-entity collaboration aspects in combination with interoperability challenges related with the integration among multiple process data collection tools and advanced planning systems. Finally, this interdisciplinary review leads to the discussion of relevant guidelines that can guide future research and integration projects in this domain.

Fuzzy Approach to Decision Support System Design for Inventory Control and Management

Abstract The ubiquitous nature of inventory and its reliance on a reliable decision support system (DSS) is crucial for ensuring continuous availability of goods. The DSS needs to be designed in a manner that enables it to highlight its present status. Further, the DSS should be able to provide indications about subtle and large-scale variations that are likely to occur in the supply chain within the context of the decision-making framework and inventory management. However, while dealing with the parameters of the system, it is observed that its operations and mechanisms are surrounded by uncertain, imprecise, and vague environments. Fuzzy-based approaches are best suited for such situations; however, these require assistance from learning systems like artificial neural network (ANN) to facilitate automated decision support. When ANN and fuzzy are combined, the fuzzy neural system and the neuro-fuzzy system (NFS) are formulated. The model of the DSS reported here is based on a framework commonly known as adaptive neuro-fuzzy inference system (ANFIS), which is a version of NFS. The configured model has the advantages of both the ANN and fuzzy systems, and has been tested for the design of a DSS for use as part of inventory control. In this work, we report the design of an ANFIS-based DSS configured to work as DSS for inventory management. The system accepts demand as input and generates procurement, ordering, and holding cost to control production and supply. The system deals with a certain profitability rating required to quantify the changes in the input and is combined with the day-to-day inventory records and demand-available cycle. The effectiveness of the system has been checked in terms of number and types of membership used, accuracy generated, and computational efficiency accounted by the computation cycles required.

Usefulness and perceived usefulness of Decision Support Systems (DSSs) in participatory forest planning: the final users’ point of view

Abstract: In recent decades, the focus of forestry Decision Support Systems (DSSs) has expanded to consider the social dimension of forestry and to support participatory decision-making. A large number of models and tools have become available to solve forest management planning problems. The Usefulness of a DSS depends on the range of tools that it incorporates, and many researches have been developed to evaluate DSSs using Usefulness as parameter. The assessment of Usefulness concerns the effectiveness of a DSS. Furthermore, most assessments take into account the degree of Perceived Usefulness, which is considered an indicator of the impact a system has on job performance. The present study focuses on the analysis of final users’ point of view on the Usefulness and Perceived Usefulness of DSSs in participatory forest planning. The research investigates how forest users’ characteristics and context influence their views on the potentialities of DSSs to enhance both the various phases of the participatory planning process (Usefulness) and job performance (Perceived Usefulness). The study is based on quantitative data collected through two questionnaires e-mailed to a sample of 150 DSSs end users. The questionnaires focused on Usefulness and on Perceived Usefulness topics, respectively. Results indicate that special attention must be given to motivating respondents with a clear explanation of the survey objectives when e-mailing questionnaires. Moreover, results show that, in general, respondents consider DSSs useful at each step of the participatory process, despite differences emerge among steps. The research also shows that respondents’ Perceived Usefulness of DSSs was higher before actually engaging with DSSs. Furthermore, the results highlight differences in Perceived Usefulness to improve job performance, suggesting that the use of DSSs may actually improve job performance more than expected. Specifically, results indicate that improving the technical descriptions of methodologies incorporated in a DSS may contribute to increasing the Perceived Usefulness. The information provided within this research contributes to the advancement of knowledge regarding the Usefulness of DSSs as perceived by forest stakeholders, which in turn supports the improvement of DSS architectures and the development of participatory processes in forest management planning.

Experimental Evidence on Decision-Making in Availability Service Level Agreements

As more enterprises buy information technology services, studying their underpinning contracts becomes more important. With cloud computing and outsourcing, these service level agreements (SLAs) are now often the only link between the business and the supporting IT services. This paper presents an experimental economics investigation of decision-making with regard to availability SLAs, among enterprise IT professionals. The method and the ecologically valid subjects make the study unique to date among IT service SLA studies. The experiment consisted of pairwise choices under uncertainty, and subjects ( $N=46$ ) were incentivized by payments based on one of their choices, randomly selected. The research question investigated in this paper is: Do enterprise IT professionals maximize expected value when procuring availability SLAs, as would be optimal from the business point of view? The main result is that enterprise IT professionals fail to maximize expected value. Whereas some subjects do maximize expected value, others are risk-seeking, risk-averse, or exhibit nonmonotonic preferences. The nonmonotonic behavior in particular is an interesting observation, which has no obvious explanation in the literature. For a subset of the subjects ( $N=29$ ), a few further hypotheses related to associations between general attitude to risk or professional experience on the one hand, and behavior in SLAs on the other hand, were investigated. No support for these associations was found. The results should be interpreted with caution, due to the limited number of subjects. However, given the prominence of SLAs in modern IT service management, the results are interesting and call for further research, as they indicate that current professional decision-making regarding SLAs can be improved. In particular, if general attitude to risk and professional experience do not impact decision-making with regard to SLAs, more extensive use of decision-support systems might be called for in order to facilitate proper risk management.