Peer-reviewed publications

More than 220 articles and abstracts about 1000minds applications in a wide variety of areas have been published since 2006.

Please send us your details if your publication isn’t listed here. Your work might inspire others!

Accounting and finance

V Narayanan, K Baird & R Tay (2020), “Investment decisions: the trade-off between economic and environmental objectives”, The British Accounting Review, early online version
M Namazi & R Gholami (2017), “Comprehensive ranking model of companies via accounting information, balanced scorecard and PAPRIKA technique”, Journal of Accounting Knowledge 7, 7-33
R Whiting, P Hansen & A Sen (2017), “A tool for measuring SMEs’ reputation, engagement and goodwill: A New Zealand exploratory study”, Journal of Intellectual Capital 18, 170-88
R Whiting, P Hansen & A Sen (2016), “A tool for measuring SMEs’ reputation, engagement and goodwill including internet and social media presence”, Proceedings of the 8th Asia-Pacific Interdisciplinary Research in Accounting (APIRA) Conference, Melbourne, Australia, 2016

Charities, donor behaviour and foreign aid

M Genç, S Knowles & T Sullivan (2020), “In search of effective altruists”, Applied Economics, early online version
S Feeny, P Hansen, S Knowles, M McGillivray & F Ombler (2019), “Donor motives, public preferences and the allocation of UK foreign aid: A discrete choice experiment approach”, Review of World Economics 155, 511-37
S Knowles (2019), “Why more Kiwis are not effective altruists”, EcoNZ@Otago 42, 8-9
N Aznam, W Hussain & F Bosli, “Preliminary study of zakat criteria to enhance existing zakat distribution methods using 1000Minds”, abstract, 1^st International Conference On Social Studies, Moral, and Character Education (ICSMC), Yogyakarta, Indonesia, 2018
H Cunningham, S Knowles & P Hansen (2017), “Bilateral foreign aid: How important is aid effectiveness to people for choosing countries to support?”, Applied Economics Letters 24, 306-10 [Economics Discussion Papers, No. 1605]
P Hansen, N Kergozou, S Knowles & P Thorsnes (2014), “Developing countries in need: Which characteristics appeal most to people when donating money?”, Journal of Development Studies 50, 1494-1509
P Hansen, N Kergozou & S Knowles (2013), “Charitable giving: How recipient-country characteristics influence donors’ behaviour”, EcoNZ@Otago 31, 1-3

Clinical guidelines

L de Llano, I Dávila, E Martínez-Moragón et al (2021), “Development of a tool to measure the clinical response to biologic therapy in uncontrolled severe asthma: the FEOS score” The Journal of Allergy and Clinical Immunology: In Practice, early online version
C Baggott, P Hansen, R Hancox et al (2020), “What matters most to patients when choosing treatment for mild-moderate asthma? Results from a discrete choice experiment”, Thorax 75, 842-48
D Pinto, S Prabhakaran, E Tipton & A Naidech (2020), “Why physicians prescribe prophylactic seizure medications after intracerebral hemorrhage: An adaptive conjoint analysis”, Journal of Stroke and Cerebrovascular Diseases 29, 104628
M Anaka, D Chan, S Pattison et al (2020), abstract, 12^th Annual Multidisciplinary Neuroendocrine Tumor Medical Symposium of the North American Neuroendocrine Tumor Society, Boston, USA, “Understanding the treatment preferences of neuroendocrine tumor patients using discrete choice experiments”, Pancreas 49, 461-91
A Liberman, D Pinto, S Rostanski et al (2019), “Clinical decision-making for thrombolysis of acute minor stroke using adaptive conjoint analysis”, The Neurohospitalist 9, 9-14
C Baggott, J Hardy, H Reddel et al (2019), abstract, 2019 European Respiratory Society (ERS) International Congress, Madrid, Spain, 2019, “Discrete choice experiments identifying attributes influencing treatment preference in mild asthma”, European Respiratory Journal 54(suppl 63), PA4189
P Teo, R Hinman, T Egerton, K Dziedzic & K Bennell (2019), “Identifying and prioritizing clinical guideline recommendations most relevant to physical therapy practice for hip and/or knee osteoarthritis”, Journal of Orthopaedic & Sports Physical Therapy 49, 501-12
R Cai, H Chaplin, P Livermore et al (2019), “Development of a benchmarking toolkit for adolescent and young adult rheumatology services (BeTAR)”, Pediatric Rheumatology 17, 23
A Liberman, D Pinto, D Labovitz, A Naidech & S Prabhakaran (2018), abstract, American Heart Association/American Stroke Association 2018 International Stroke Conference and State-of-the-Science Stroke Nursing Symposium, Los Angeles, USA, 2018, “Evaluating thrombolysis decision making in minor stroke using adaptive discrete choice experimentation”, Stroke 49(suppl 1)
J Hart, R Hinman, A Van Ginckel et al (2018), “Factors influencing cane use for the management of knee osteoarthritis: a cross sectional survey”, Arthritis Care & Research 70, 1455-60

D Pinto, M Danilovich, P Hansen et al (2017), “Qualitative development of a discrete choice experiment for physical activity interventions to improve knee osteoarthritis”, Archives of Physical Medicine and Rehabilitation 98, 1210-16
D Pinto, R Chang, U Bockenholt et al (2017), “What physical activity program features are important to patients with knee osteoarthritis? A discrete choice experiment”, poster abstract, World Congress on Osteoporosis, Osteoarthritis and Musculoskeletal Diseases 2017, Florence, Italy, Osteoporosis International 28(suppl1), S516-17
D Pinto, U Bockenholt, R Chang et al (2017), “Preferences for physical activity: A discrete choice experiment in people with chronic knee pain”, abstract, 2017 ACR/ARHP Annual Meeting, San Diego, USA, 2017, Arthritis & Rheumatology 69(suppl10)
D Griffin, E Dickenson, J O’Donnell et al (2016), “The Warwick Agreement on femoroacetabular impingement syndrome (FAI syndrome): an international consensus statement”, British Journal of Sports Medicine 50, 1169-76
R Walsh, B Aliarzadeh & C Mastrogiacomo (2016), “Patient strength of preference for best practices in patient education”, Journal of Community Medicine & Health Education 6, 1-8
P Nicolson, S French, R Hinman et al (2014), “Developing key messages for people with osteoarthritis: A delphi study”, Osteoarthritis & Cartilage 22, S305-S306
S French, K Bennell, P Nicolson et al (2014), “What do people with knee or hip osteoarthritis need to know? An international consensus list of essential statements for osteoarthritis”, Arthritis Care & Research 67, 809-16

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Corporate strategic management

J Ruhland (2006), “Strategic mobilization: What strategic management can learn from social movement research”, Management 11, 23-31

Covid-19 prioritization

M Roy, P Hansen, T Sullivan, F Ombler, M Kiore & A Stapleton (2021), “Rapid development of a tool for prioritizing Covid-19 patients for intensive care” Critical Care Explorations 3, e0368
P De Nardo, E Gentilotti, F Mazzaferri et al and COVID-19 MCDA Group (2020), “Multi-Criteria Decision Analysis to prioritize hospital admission of patients affected by COVID-19 in low-resource settings with hospital-bed shortage”, International Journal of Infectious Diseases 98, 494-500

Decision-making methods and software

S Kadenko, V Tsyganok, O Andriichuk & A Karabchuk (2020), “Analysis of decision support tools in the context of solving strategic planning tasks” (in Ukrainian), Registration, Storage and Data Processing 22, 77-91
V Lakhno, T Kartbayev, A Turginbayeva, Zh Alimseitova & G Beketova (2020), “Analysis of existing and development prospects of decision support systems for evaluating investment projects in the field of enterprise digitalization”, International Journal of Advanced Trends in Computer Science and Engineering 9, 8533-39
B Németh, A Molnár, S Bozóki et al (2019), “Comparison of weighting methods used in multicriteria decision analysis frameworks in healthcare with focus on low-and middle-income countries”, Journal of Comparative Effectiveness Research 8, 195-204
F Vergara-Solana, M Araneda & G Ponce-Díaz (2019), “Opportunities for strengthening aquaculture industry through multicriteria decision-making”, Reviews in Aquaculture 11, 105-18
S Mirzaee, M Ruth & D Fannon (2019), “Reconciling diverse perspectives of decision makers on resilience and sustainability”, Chapter 19, In: M Ruth & S Goessling-Reisemann, Handbook on Resilience of Socio-Technical Systems, Elgar, 360-86
S Gafar (2018), “Decision support top software products: A review”, Engineering and Technology Journal 3, 416-20
A Kumar, B Sah, A Singh et al (2017), “A review of multi criteria decision making (MCDM) towards sustainable renewable energy development”, Renewable and Sustainable Energy Reviews 69, 596-609
J Mustajoki & M Marttunen (2017), “Comparison of multi-criteria decision analytical software for supporting environmental planning processes”, Environmental Modelling & Software 93, 78-91
N Grima, S Singh & B Smetschka (2017), “Decision making in a complex world: Using OPTamos in a multi-criteria process for land management in the Cuitzmala watershed in Mexico”, Land Use Policy 67, 73-85
HR Weistroffer & Y Li (2016), “Multiple criteria decision analysis software”, Chapter 12, In: S Greco, M Ehrgott & JR Figueira (editors), Multiple Criteria Decision Analysis. State of the Art Surveys, International Series in Operations Research & Management Science 233, 1301-41

R Sengupta (2016), “Other decision-making models”, Chapter 5, In: Decision Sciences: Theory and Practice, CRC Press
U Baizyldayeva, O Vlasov, A Kuandykov & T Akhmetov (2013), “Multi-criteria decision support systems. Comparative analysis”, Middle-East Journal of Scientific Research 16, 1725-30

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Disaster recovery

R Jana, C Prakash & A Tiwari (2019), “Humanitarian aid delivery decisions during the early recovery phase of disaster using a discrete choice multi-attribute value method”, Annals of Operations Research 283, 1211-25

Disease classification and diagnosis

C Shiboski & T Daniels (2021), “Historical background, classification, and diagnostic criteria”, Section 1.2, In: E Price & A Tappuni (editors), Oxford Textbook of Sjögren's Syndrome, Oxford University Press
M Tolend, T Junhasavasdikul, R Cron et al (2021), “Discrete choice experiment on a magnetic resonance imaging scoring system for temporomandibular joints in juvenile idiopathic arthritis” Arthritis Care & Research, early online version
N Dalbeth, L Stamp & W Taylor (2020), “What is remission in gout and how should we measure it?”, Rheumatology, early online version
M Aringer, C Costenbader, D Daikh, et al (2019), “2019 European League Against Rheumatism/American College of Rheumatology classification criteria for systemic lupus erythematosus”, Arthritis & Rheumatology 71, 1400-12
S Tedeschi, S Johnson, D Boumpas et al (2019), “Multicriteria decision analysis process to develop new classification criteria for systemic lupus erythematosus”, Annals of the Rheumatic Diseases 78, 634-40
T Ribeiro, A Abad & B Feldman (2019), “Developing a new scoring scheme for the Hemophilia Joint Health Score 2.1”, Research and Practice in Thrombosis and Haemostasis 3, 405-11
Z Wallace, R Naden, S Chari et al (2019), “The 2019 American College of Rheumatology/European League Against Rheumatism Classification Criteria for IgG4‐Related Disease”, Arthritis & Rheumatology, early online version
L Rider, R Aggarwal, P Machado et al (2018), “Update on outcome assessment in myositis”, Nature Reviews Rheumatology 14, 303-18
S Rosina, G Varnier, M Mazzoni et al (2018), “Innovative research design to meet the challenges of clinical trials for juvenile dermatomyositis”, Current Rheumatology Reports 20, 29
C Shiboski, S Shiboski, R Seror et al and the International Working Group on SS Classification Criteria (2017), “2016 American College of Rheumatology/European League Against Rheumatism: Classification Criteria for primary Sjögren’s Syndrome: A consensus and data-driven methodology involving three international patient cohorts”, Annals of Rheumatic Diseases 76, 9-16

C Shiboski, S Shiboski, R Seror et al and the International Working Group on SS Classification Criteria (2017), “2016 American College of Rheumatology/European League Against Rheumatism Classification Criteria for primary Sjögren’s Syndrome: A consensus and data-driven methodology involving three international patient cohorts”, Arthritis & Rheumatology 69, 35-45
E Miloslavsky, R Naden, J Bijlsma et al (2017), “Development of a Glucocorticoid Toxicity Index (GTI) using multicriteria decision analysis”, Annals of the Rheumatic Diseases 76, 543-46
J Kuemmerle-Deschner, S Ozen, P Tyrrell et al (2017), “Diagnostic criteria for cryopyrin-associated periodic syndrome (CAPS)”, Annals of the Rheumatic Diseases 76, 942-77
L Rider, N Ruperto, R Aggarwal et al (2017), “2016 ACR-EULAR adult dermatomyositis and polymyositis and juvenile dermatomyositis response criteria – methodological aspects”, Rheumatology 65, 1884-93
L Rider, R Aggarwal, A Pistorio et al for the International Myositis Assessment and Clinical Studies Group and the Paediatric Rheumatology International Trials Organization (2017), “American College of Rheumatology/European League Against Rheumatism criteria for minimal, moderate, and major clinical response in juvenile dermatomyositis”, Annals of the Rheumatic Diseases 76, 782-91 News »
L Rider, R Aggarwal, A Pistorio et al for the International Myositis Assessment and Clinical Studies Group and the Paediatric Rheumatology International Trials Organization (2017), “2016 American College of Rheumatology/European League Against Rheumatism Criteria for minimal, moderate, and major clinical response in juvenile dermatomyositis: An International Myositis Assessment and Clinical Studies Group/Paediatric Rheumatology International Trials Organization Collaborative Initiative”, Arthritis & Rheumatology 69, 911-23 News »
N ter Haar, K Annink, M Sulaiman et al (2017), “Development of the autoinflammatory disease damage index (ADDI)”, Annals of the Rheumatic Diseases 76, 821-30 News »
R Aggarwal, L Rider, N Ruperto et al for the International Myositis Assessment and Clinical Studies Group and the Paediatric Rheumatology International Trials Organization (2017), “2016 American College of Rheumatology/European League Against Rheumatism criteria for minimal, moderate, and major clinical response in adult dermatomyositis and polymyositis”, Annals of the Rheumatic Diseases 76, 792-801 News »
R Aggarwal, L Rider, N Ruperto et al for the International Myositis Assessment and Clinical Studies Group and the Paediatric Rheumatology International Trials Organization (2017), “2016 American College of Rheumatology/European League Against Rheumatism criteria for minimal, moderate, and major clinical response in adult dermatomyositis and polymyositis: An International Myositis Assessment and Clinical Studies Group/Paediatric Rheumatology International Trials Organization Collaborative Initiative”, Arthritis & Rheumatology 69, 898-910 News »
S Tedeschi, S Johnson, D Boumpas et al (2017), “Multicriteria decision analysis for developing new classification criteria for systemic lupus erythematosus”, oral presentation, Annual European Congress of Rheumatology, Madrid, Spain, 2017, Annals of the Rheumatic Diseases 76(suppl2), 50 News »
A Vargas-Santos, W Taylor & T Neogi (2016), "Gout classification criteria: Update and implications", Current Rheumatology Reports 18, 1-10
H de Lautour, W Taylor, A Adebajo et al (2016), “Development of preliminary remission criteria for gout using Delphi and 1000minds® consensus exercises”, Arthritis Care & Research 68, 667-72
M Aringer, T Dörner, N Leuchten & S Johnson (2016), “Toward new criteria for systemic lupus erythematosus – a standpoint”, Lupus 25, 805-11
M.L Avila, L Brandão, S Williams et al (2016), “Development of CAPTSure™–a new index for the assessment of pediatric postthrombotic syndrome”, Journal of Thrombosis and Haemostasis 14, 2376-85
W Taylor (2016), “Pros and cons of conjoint analysis of discrete choice experiments to define classification and response criteria in rheumatology”, Current Opinion in Rheumatology 28, 117-21
D Aletaha (2015), “Classification of rheumatoid arthritis”, pp. 3-21, In: P Emery (editor), Atlas of Rheumatoid Arthritis, Springer
J Kuemmerle-Deschner, S Ozen, P Tyrrell et al (2015), “Development and validation of diagnostic criteria for cryopyrin associated periodic syndromes”, abstract, 2015 ACR/ARHP Annual Meeting, San Francisco, USA, 2015, Arthritis & Rheumatology 67(suppl10) News »
J Pope & S Johnson (2015), “New classification criteria for systemic sclerosis (scleroderma)”, Rheumatic Disease Clinics of North America 41, 383-98
J Vencovsky on behalf of ACR-EULAR Myositis Response Criteria Project (2015), “New ACR/EULAR response criteria for myositis”, abstract, 2015 EULAR Annual European Congress of Rheumatology, Rome, Italy, 2015, Annals of the Rheumatic Diseases 74(suppl2), 42-43
K Annink, N ter Haar, G Gattorno et al (2015), “Development of the autoinflammatory disease damage index (ADDI)”, poster presentation, 8^th International Congress of Familial Mediterranean Fever and Systemic Autoinflammatory Diseases, Dresden, Germany, 2015, Pediatric Rheumatology 13(suppl1), P29
S Johnson (2015), “New ACR EULAR Guidelines for Systemic Sclerosis Classification”, Current Rheumatology Reports 17, 1-8
T Neogi, T Jansen, N Dalbeth et al (2015), “2015 Gout Classification Criteria: An American College of Rheumatology/European League Against Rheumatism Collaborative Initiative”, Arthritis & Rheumatology 67, 2557-68
T Neogi, T Jansen, N Dalbeth et al (2015), “2015 Gout Classification Criteria: An American College of Rheumatology/European League Against Rheumatism Collaborative Initiative”, Annals of the Rheumatic Diseases 74, 1789-98
H de Lautour, N Dalbeth & W Taylor (2014), “Development of preliminary remission criteria for gout using Delphi and 1000minds consensus exercises”, abstract, 2014 ACR/ARHP Annual Meeting, Boston, USA, 2014, Arthritis & Rheumatology 66(suppl), S68
L Rider, R Aggarwal, N Bayat et al (2014), “A hybrid conjoint analysis model is proposed as the definition of minimal, moderate and major clinical improvement in juvenile dermatomyositis clinical trials”, abstract, 2014 ACR/ARHP Annual Meeting, Boston, USA, 2014, Arthritis & Rheumatology 66(suppl), S404-5
R Aggarwal, L Rider, N Ruperto et al (2014), “A consensus hybrid definition using a conjoint analysis is proposed as response criteria for minimal and moderate improvement in adult polymyositis and dermatomyositis clinical trials”, abstract, 2014 ACR/ARHP Annual Meeting, Boston, USA, 2014, Arthritis & Rheumatology 66(suppl), S404-5
S Johnson, R Naden, J Fransen et al (2014), “Multicriteria decision analysis methods with 1000minds for developing systemic sclerosis classification criteria”, Journal of Clinical Epidemiology 67, 706-14
F Dobson, R Hinman, E Roos et al (2013), “OARSI recommended performance-based tests to assess physical function in people with established hip and knee osteoarthritis”, abstract, 2013 Osteoarthritis Research Society International (OARSI) World Congress, Philadelphia, USA, Osteoarthritis & Cartilage 21(suppl), S39-S40
F Dobson, R Hinman, E Roos et al (2013), “OARSI recommended performance-based tests to assess physical function in people diagnosed with hip or knee osteoarthritis”, Osteoarthritis & Cartilage 21, 1042-52 (manual)
F van den Hoogen, D Khanna, J Fransen et al (2013), “Classification criteria for systemic sclerosis: Preliminary results”, abstract, 2014 Annual European Congress of Rheumatology, Madrid, Spain, 2013, Annals of the Rheumatic Diseases 72(suppl), A59
F van den Hoogen, D Khanna, J Fransen et al (2013), “2013 classification criteria for systemic sclerosis: an American College of Rheumatology/European League Against Rheumatism collaborative initiative”, Annals of the Rheumatic Diseases 72, 1747-55
F van den Hoogen, D Khanna, J Fransen et al (2013), “2013 classification criteria for systemic sclerosis: an American College of Rheumatology/European League Against Rheumatism collaborative initiative”, Arthritis & Rheumatism 65, 2737-47
J Pope, D Khanna, J Fransen et al (2013), “The ACR/EULAR Classification Criteria for Systemic Sclerosis (SSc)”, CRA Abstracts, Canadian Rheumatology Association (CRA) Annual Scientific Meeting, Ottawa, Canada, 2013, The Journal of Rheumatology, 40, 951
M Vinall (2013), “2013 ACR-EULAR Scleroderma Classification Criteria”, MD Conference Express 13, 12-13
W Taylor, M Brown, O Aati et al (2013), “Do patient preferences for core outcome domains for chronic gout studies support the validity of composite response criteria?”, Arthritis Care & Research 65, 1259-64
J Pope, J Fransen, S Johnson et al (2012), “Report from the EULAR ACR Scleroderma Classification Criteria Committee”, Rheumatology 51(suppl 2), ii1
B Combe (2011), “The new classiﬁcation criteria for rheumatoid arthritis and their impact on therapeutic decisions”, Editorial, Joint Bone Spine 78, 539-41
W Taylor, J Singh, K Saag et al (2011), “Bringing it all together: A novel approach to the development of response criteria for chronic gout clinical trials” The Journal of Rheumatology 38, 1467-70
A Saraux, G Tobon, S Jousse-Joulin & V Devauchelle-Pensec (2010), “Les critères de classification et/ou de prédiction de la polyarthrite rhumatoïde”, Rhumatisme Monographies 77, 12-16
D Aletaha, T Neogi, A Silman et al (2010), “2010 Rheumatoid arthritis classification criteria: An American College of Rheumatology / European League Against Rheumatism collaborative initiative”, Annals of the Rheumatic Diseases 69, 1580-88
D Aletaha, T Neogi, A Silman et al (2010), “2010 Rheumatoid arthritis classification criteria: An American College of Rheumatology / European League Against Rheumatism collaborative initiative”, Arthritis & Rheumatism 62, 2569-81 News »
E Stanisławska-Biernat, M Sierakowska & S Sierakowski (2010), “Recommendations for diagnosis and treatment. New rheumatoid arthritis classification criteria”, Reumatologia 48, 361-65
J Kay & K Upchurch (2012), “ACR/EULAR 2010 rheumatoid arthritis classification criteria”, Rheumatology 51, vi5-vi9
M Mjaavatten & V Bykerk (2013), “Early rheumatoid arthritis: The performance of the 2010 ACR/EULAR criteria for diagnosing RA”, Best Practice & Research Clinical Rheumatology 27, 251-6
T Neogi, D Aletaha, D Silman et al (2010), “The 2010 American College of Rheumatology / European League Against Rheumatism classification criteria for rheumatoid arthritis: Phase 2 methodological report”, Arthritis & Rheumatism 62, 2582-91

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Disease R&D prioritization

S Babashahi, P Hansen & T Sullivan (2021), “Creating a priority list of non-communicable diseases to support health research funding decision-making”, Health Policy 125, 221-28
D Hunter, P Nicolson, C Little et al (2019), “Developing strategic priorities in osteoarthritis research: Proceedings and recommendations arising from the 2017 Australian Osteoarthritis Summit”, BMC Musculoskeletal Disorders 20, 74
Y Mei, S Guan, H Zhang et al (2018), abstract, 2018 OARSI World Congress on Osteoarthritis, Liverpool, UK, 2018, “Priorities for osteoarthritis research in China”, Osteoarthritis & Cartilage 26(suppl 1), S220
E Tacconelli, E Carrara, A Savoldi et al and the WHO Pathogens Priority List working group (2018), “Discovery, research, and development of new antibiotics: the WHO priority list of antibiotic-resistant bacteria and tuberculosis”, The Lancet Infectious Diseases 18, 318-27 News1 » News2 » News3 »
P Hansen (2018), “The world’s deadliest diseases: The WHO priority list of antibiotic-resistant bacteria”, EcoNZ@Otago 40, 4-6
Y Mei, S Guan, H Zhang, D Hunter, Z Zhang (2018), abstract, Annual European Congress of Rheumatology, EULAR 2018, Amsterdam, The Netherlands, 2018, “Priorities for osteoarthritis research should be done in China”, Annals of the Rheumatic Diseases 77(suppl 2), 1613
E Tacconelli, N Magrini & Co-ordinating Group, “Global priority list of antibiotic-resistant bacteria to guide research, discovery, and development of new antibiotics”, WHO Short Summary, 2017 News1 » News2 »
European Centre for Disease Prevention and Control (2017), ECDC Tool for the Prioritization of Infectious Disease Threats – Handbook and Manual, ECDC
K Weyer, E Tacconelli, N Magrini & Co-ordinating Group (2017), “Prioritization of pathogens to guide discovery, research and development of new antibiotics for drug-resistant bacterial infections, including tuberculosis”, World Health Organization (WHO/EMP/IAU/2017.12) News3 »
S French, P Beliveau, P Bruno et al (2017), “Research priorities of the Canadian chiropractic profession: a consensus study using a modified Delphi technique”, Chiropractic & Manual Therapies 25, 38

Education

K Duncan (2020), “Using conjoint analysis to prioritize college student preferences in the time of COVID-19”, Journal of Higher Education Management 35, 35-43
H Mohammed, E AL-Dahneem, & A Hamadi (2016), “A comparative analysis for adopting an innovative pedagogical approach of flipped teaching for active classroom learning”, Journal of Global Business and Social Entrepreneurship 3, 86-94

Energy use

W Ogden & P Thorsnes (2019), “Electric or petrol/diesel? Which car would you choose?”, EcoNZ@Otago 42, 1-4
R Ford, S Walton, J Stephenson et al (2016), “Emerging energy transitions: PV uptake beyond subsidies”, Technological Forecasting and Social Change 117, 138-50
R Ford, O Sumavsk, A Clarke & P Thorsnes (2014), “Personalized energy priorities: a user-centric application for energy advice”, In: A Marcus (editor), Design, User Experience, and Usability. User Experience Design for Everyday Life Applications and Services, Lecture Notes in Computer Science 8519, 542-53
R Pomeroy (2013), “Harvesting solar energy in sunny Dunedin”, EcoNZ@Otago 31, 9-11

Environment

T Derkley, D Biggs, M Holden & C Phillips (2019), “A framework to evaluate animal welfare implications of policies on rhino horn trade”, Biological Conservation 235, 236-49
E de Olde, H Moller, F Marchand et al (2017), “When experts disagree: The need to rethink indicator selection for assessing sustainability of agriculture”, Environment, Development and Sustainability 19, 1327–42
S Chhun, V Kahui & P Thorsnes (2015), “Advancing marine policy towards ecosystem based management by eliciting public preferences”, Marine Resources Economics 30, 261-75
G Crozier & A Schulte-Hostedde (2014), “Towards improving the ethics of ecological research”, Science and Engineering Ethics, 1-18
P Graff & S McIntyre (2014), “Using ecological attributes as criteria for the selection of plant species under three restoration scenarios”, Austral Ecology 39, 907-17
S Chhun, P Thorsnes & H Moller (2013), “Preferences for management of near-shore marine ecosystems: A choice experiment in New Zealand”, Resources 2, 406-38
P Boyd, C Law & S Doney (2011), “Commentary: A climate change atlas for the ocean” Oceanography 24, 13-16

Forestry

M Kühmaier, H Harrill, M Ghaffariyan et al (2019), “Using conjoint analyzes to improve cable yarder design characteristics: An Austrian yarder case study to advance cost-effective extraction”, Forests, 10, 165

Health preferences research

A Pathak, S Sharma, A Heinemann et al (2020), “Development and assessment of a verbal response scale for the Patient-Specific Functional Scale (PSFS) in a low-literacy, non-western population”, Quality of Life Research, early online version
T Sullivan, P Hansen, F Ombler, S Derrett & N Devlin (2020), “A new tool for creating personal and social EQ-5D-5L value sets, including valuing ‘dead’”, Social Science & Medicine 246, 112707
P Hansen, F Ombler & T Sullivan (2019), “An online tool for valuing people’s health, including valuing ‘dead’”, EcoNZ@Otago 43, 6-9
R Norman, B Craig, P Hansen et al (2019), “Issues in the design of discrete choice experiments”, The Patient – Patient-Centered Outcomes Research 12, 281-85
F Ombler, M Albert & P Hansen (2018), “How significant are ‘high’ correlations between EQ-5D value sets?”, Medical Decision Making 38, 635-45

Health technology prioritization

J Chua, P Hansen, A Briggs et al (2020), “Stakeholders’ preferences for osteoarthritis interventions in health services: a cross-sectional study using multi-criteria decision analysis”, Osteoarthritis and Cartilage Open 2, 100110
V Lvovschi, M Maignan, K Tazarourte et al (2020), “Multiple criteria decision analysis approach to consider therapeutic innovations in the emergency department: The methoxyflurane organizational impact in acute trauma pain”, PLOS One 15, e0231571
A Moreno-Calderón, T Tong & P Thokala (2019), “Multi-criteria Decision Analysis software in healthcare priority setting: A systematic review”, PharmacoEconomics, early online version
J Chua, P Hansen, A Briggs, & J Abbott (2019), abstract, 2019 OARSI World Congress on Osteoarthritis: Promoting Clinical and Basic Research in Osteoarthritis, Toronto, Canada, 2019, “What attributes of interventions for osteoarthritis drive preferences? A discrete choice experiment involving cross-sectoral and multi-disciplinary stakeholder groups”, Osteoarthritis and Cartilage 27, S302
J Chua, P Hansen, A Briggs, R Wilson, D Gwynne-Jones & J Abbott (2019), abstract, 2019 OARSI World Congress on Osteoarthritis: Promoting Clinical and Basic Research in Osteoarthritis, Toronto, Canada, 2019, “Integrating values and preferences with the best available evidence: a multi-criteria decision analysis approach”, Osteoarthritis and Cartilage 27, S308-S309
J Eyles, D Hunter, K Bennell et al (2019), “Priorities for the effective implementation of osteoarthritis management programs: an OARSI international consensus exercise”, Osteoarthritis and Cartilage 27, 1270-79
J Eyles, K Bennell, K Dziedzic et al (2019), abstract, 2019 OARSI World Congress on Osteoarthritis: Promoting Clinical and Basic Research in Osteoarthritis, Toronto, Canada, 2019, “Implementation priorities for osteoarthritis management programs”, Osteoarthritis and Cartilage 27, S307-S308
P Hansen & N Devlin (2019), “Multi-Criteria Decision Analysis (MCDA) in health care decision making”, In: Oxford Research Encyclopedia of Economics and Finance, Oxford University Press
S Babashahi (2019), “Using Multiple Criteria Decision Analysis (MCDA) to create a priority list of chronic noncommunicable diseases (CNCD) to guide health research spending”, abstract, 2019 International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Europe Conference, Copenhagen, Denmark, Value in Health 22(Suppl 3), S712
S Howard, I Scott, H Ju, L McQueen & P Scuffham (2019), “Multicriteria decision analysis (MCDA) for health technology assessment: the Queensland Health experience”, Australian Health Review 43, 591-99

I Lasorsa, E Padoano, S Marceglia & A Accardo (2018), “Multi-criteria decision analysis for the assessment of non-clinical hospital services: Methodology and case study”, Operations Research for Health Care 23, early online version
M Espinoza, R Rojas & H de Patiño (2018), “Knowledge translation in practice: Exploring the potential use of MCDA in Central America and the Caribbean”, Value in Health Regional Issues 17, 148-9
A Shmueli (2017), “Do the equity-efficiency preferences of the Israeli Basket Committee match those of Israeli health policy makers?”, Israel Journal of Health Policy Research 6, 20
A Shmueli, O Golan, F Paolucci & E Mentzakis (2017), “Efficiency and equity considerations in the preferences of health policymakers in Israel”, Israel Journal of Health Policy Research 6, 18
J Drake, J Carlos T de Hart, C Monleón, W Toro & J Valentim (2017), “Utilization of multiple-criteria decision analysis (MCDA) to support healthcare decision-making FIFARMA, 2016”, Journal of Market Access & Health Policy 5, 1360545
T Sullivan & P Hansen (2017), “Determining criteria and weights for prioritizing health technologies based on the preferences of the general population: A New Zealand pilot study”, Value in Health 20, 679-86
N Martelli, P Hansen, H van den Brink et al (2016), “Combining multi-criteria decision analysis and mini-health technology assessment: A funding decision-support tool for medical devices in a university hospital setting”, Journal of Biomedical Informatics 59, 201-08
I Lasorsa, G Abis, B Podda & A Accardo (2015), “Multi-criteria decision analysis to redesign an Italian Clinical Engineering Service under specific needs and regulation requirements”, In: D Jaffray (editor), World Congress on Medical Physics and Biomedical Engineering, 2015, Toronto, Canada, IFMBE Proceedings 51, Springer International Publishing, 1562-5
T Sullivan & P Hansen (2015), “Which drugs, medical procedures and equipment should be funded?”, EcoNZ@Otago 34, 8-12
T Sullivan & P Hansen (2014), “Determining benefits-related criteria and weights for prioritizing health technologies”, Occasional Report, 14/01, Centre for Health Systems, University of Otago
O Golan & P Hansen (2012), “Which health technologies should be funded? A prioritization framework based explicitly on value for money”, Israel Journal of Health Policy Research 1, 44 News »
N Devlin & J Sussex (2011), “Incorporating multiple criteria in HTA. Methods and processes”, OHE Report, Office of Health Economics
O Golan, P Hansen, G Kaplan & O Tal (2011), “Health technology prioritization: Which criteria for prioritizing new technologies and what are their relative weights?” Health Policy 102, 126-35

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Information and Communications Technology (ICT)

L Romeo, J Loncarski, M Paolanti et al (2020), “Machine learning-based design support system for the prediction of heterogeneous machine parameters in Industry 4.0”, Expert Systems with Applications 140, 1128691
H Alabool, A Kamil, N Arshad & D Alarabiat (2018), “Cloud service evaluation method-based multi-criteria decision-making: A systematic literature review”, Journal of Systems and Software, 139, 161-88
H Bansal, P Shukla & M Dhar (2018), “Trust and Credibility Analysis of Websites: Role of trust and credibility in evaluating online content”, Chapter 13, In: H Bansal, G Shrivastava, G Nguyen, L-M Stanciu, Social Network Analytics for Contemporary Business Organizations, IGI Global, pp 259-86
G Hernández-Ledesma, E Ramos, C Fernández-y-Fernández et al (2017), “Selection of best software engineering practices: A Multi-Criteria Decision Making approach”, Research in Computing Science 136, 47-60
S Alismaili, M Li, J Shen & Q He (2017), “A consumer-oriented decision-making approach for selecting the cloud storage service: From PAPRIKA perspective”, In: M Fan, J Heikkilä, H Li, M Shaw & H Zhang (editors), pp. 1-12, Internetworked World, Lecture Notes in Business Information Processing 296, Revised Selected Papers from 15th Workshop on e-Business, WeB 2016, Dublin, Ireland, 2016
S Al Isma’ili, M Li, J Shen & Q He (2016), “Cloud computing adoption decision modelling for SMEs: a conjoint analysis”, International Journal of Web and Grid Services 12, 296-327
S Alismaili, M Li & J Shen (2016), “Cloud computing adoption decision modelling for SMEs: From the PAPRIKA perspective”, In: J Hung, N Yen & K-C Li (editors), pp. 597-615, Frontier Computing: Theory, Technologies and Applications, Lecture Notes in Electrical Engineering 375, proceedings of the 4th International Conference on Frontier Computing, Bangkok, Thailand, 2015
A Mancini, E Frontoni & P Zingaretti (2015), “Embedded multisensor system for safe point-to-point navigation of impaired users”, IEEE Transactions on Intelligent Transportation Systems, 16, 3543-55
D Kalra & M Birdi (2015), “Differentiating algorithms of cloud task scheduling based on various parameters”, IOSR Journal of Computer Engineering, 17, 35-38
S Aggarwal, H Van Oostendorp, Y Reddy & B Indurkhya (2014), “Providing web credibility assessment support”, Proceedings of the 2014 European Conference on Cognitive Ergonomics, Vienna, Austria, 2014

H Lawrence & S Silas (2013), “Efficient Qos based resource scheduling using PAPRIKA method for cloud computing”, International Journal of Engineering Science & Technology 5, 638-43
S Aggarwal & H Van Oostendorp (2011), “An attempt to automate the process of source evaluation”, ACEEE International Journal on Communication 2, 18-20
S Aggarwal & H Van Oostendorp (2011), “An attempt to automate the process of source evaluation”, International Conference on Advances in Computer Engineering, Trivandrum, 2011, ACE Proceedings 2011, 49-51

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Infrastructure

S Long, E Ng & C Downing, “Strategic-based multi-criteria decision making", Proceedings of the American Society for Engineering Management 2014 International Annual Conference, Virginia, USA, 2014

Marketing research

M Mirosa, Y Liu & P Bremer (2020), “Determining how Chinese consumers that purchase Western food products prioritize food safety cues: A conjoint study on adult milk powder”, Journal of Food Products Marketing 26, 358-71
T Phan, P Bremer & M Mirosa (2020), “Vietnamese consumers’ preferences for functional milk powder attributes: A segmentation-based conjoint study with educated consumers”, Sustainability 12, 5258
C Parsad, C Chandra & S Suman (2019), “A product feature prioritization-based segmentation model of consumer market for health drink”, International Journal of Strategic Decision Sciences 10, 70-83
R Wijland, P Hansen & F Gardezi (2016), “Mobile nudging: Youth engagement with banking apps”, Journal of Financial Services Marketing 21, 51-63
R Wijland, P Hansen & F Gardezi (2016), “Economic psychology applied to business: Designing a mobile-banking app”, EcoNZ@Otago 37, 12-14
PY Lee, K Lusk, M Mirosa & I Oey (2015), “An attribute prioritization-based segmentation of the Chinese consumer market for fruit juice”, Food Quality and Preference 46, 1-8

Monetary policy research

C Smith (2009), “Revealing monetary policy preferences”, Reserve Bank of New Zealand Discussion Paper Series DP2009/01

Organizational creativity

F Martins, E Santos & L Vils (2017), “Organizational creativity in innovation – a multicriteria decision analysis”, Independent Journal of Management & Production 8, 1223-45

Patient prioritization

D Gwynne‐Jones, R Wilson & C McEwan (2020), “National Referral Prioritization tool for first specialist assessment: Results of a pilot study in orthopaedic surgery”, ANZ Journal of Surgery 90, 1738-42
G Srikumar, T Eglinton & A MacCormick (2020), “Development of the General surgery prioritization tool implemented in New Zealand in 2018”, Health Policy 124, 1043-49
R Hunter, N Buckley, E Fitzgerald, A MacCormick & T Eglinton (2018), “General Surgery Prioritization Tool: a pilot study”, ANZ Journal of Surgery 88, 1279-83
D Gwynne-Jones, E Iosua & K Stout (2016), “Rationing for total hip and knee replacement using the New Zealand Orthopaedic Association (NZOA) score: Effectiveness and comparison with patient reported scores”, The Journal of Arthroplasty 31, 957-62
D White, K Solanki, V Quincey et al (2015), “Development of a multi-dimensional additive points system for determining access to rheumatology services”, Journal of Clinical Rheumatology 21, 239-43 News »
D White, R Naden, A Doube et al (2014), “Development of a referral triage system for determining access to a public hospital rheumatology service”, ARA Scientific Posters, Australian Rheumatology Association-Rheumatology Health Professionals Association 55th Annual Scientific Meeting, Hobart, Australia, Internal Medicine Journal 44(suppl S2), 10-37 News »
J Blackett, A Carslaw, D Lees et al (2014), “Rationing for total hip and knee replacement using the New Zealand Orthopaedic Association (NZOA) score: Effectiveness and comparison with patient reported scores”, New Zealand Medical Journal (Online), 127, 45-53
P Hansen, A Hendry, R Naden, F Ombler & R Stewart (2012), “A new process for creating points systems for prioritizing patients for elective health services”, Clinical Governance: An International Journal 17, 200-9
A Fitzgerald, C De Coster, S McMillan et al (2011), “Relative urgency for referral from primary care to rheumatologists: The priority referral score”, Arthritis Care & Research 63, 231-39
RAH Stewart, A Hamer, B Mahon et al (2010), “Comparison of a clinical score with individual clinician judgment for assigning priority for heart valve surgery”, abstract (poster), European Society of Cardiology Congress, Stockholm, Sweden, 2010, European Heart Journal 31 (suppl 1), 71

W Taylor & G Laking (2010), “Value for money – recasting the problem in terms of dynamic access prioritization”, Disability & Rehabilitation 32, 1020-27
A Fitzgerald, B Conner Spady, C De Coster et al (2009), “WCWL Rheumatology Priority Referral Score reliability and validity testing”, abstract, The 2009 ACR/ARHP Annual Scientific Meeting, Philadelphia, USA, Arthritis & Rheumatology 60 (suppl 10), 54
T Nosewothy, C De Coster & R Naden (2009), “Priority-setting tools for improving access to medical specialists”, poster presentation, 6^th Health Technology Assessment International Annual Meeting, Singapore, 2009, Annals, Academy of Medicine, Singapore 38, S78
P Hansen & F Ombler (2008), “A new method for scoring multi-attribute value models using pairwise rankings of alternatives”, Journal of Multi-Criteria Decision Analysis 15, 87-107

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Plant and animal breeding

K Devani, C Quinton, J Archer et al (2021), “Estimation of economic value for efficiency and animal health and welfare traits, teat and udder structure, in Canadian Angus cattle”, Journal of Animal Breeding and Genetics, early online version
J Pryce, T Nguyen, M Axford, G Nieuwhof & M Shaffer (2018), “Symposium review: Building a better cow – The Australian experience and future perspectives”, Journal of Dairy Science 101, 3702-13
M Mehar , M Wagdy, C McDougall & J Benzie (2018), “Gender differentiated needs and preferences of farmers for rohu fish in Bangladesh and India”, abstract, p. 17, In: N Gopal, M Williams & K Kusakabe (editors), GAF7: Expanding the Horizons. Book of Abstracts, The 7^th Global Conference on Gender in Aquaculture & Fisheries, Bangkok, Thailand, 2018
M Ragot, M Bonierbale & E Weltzien (2018), “From market demand to breeding decisions: A framework”, CGIAR Gender and Breeding Initiative Working Paper 2
M Slagboom, M Kargo, D Edwards et al (2016), “Preferences for breeding goal traits for Danish red and jersey cattle”, Book of Abstracts of the 67^th Annual Meeting of the European Association for Animal Production (EAAP), Belfast, UK, 2016
M Slagboom, M Kargo, D Edwards et al (2016), “Organic dairy farmers put more emphasis on production traits than conventional farmers”, Journal of Dairy Science 99, 9845-56
M Slagboom, M Kargo, D Edwards et al (2016), “Herd characteristics influence farmers’ preferences for trait improvements in Danish Red and Danish Jersey cows”, Acta Agriculturae Scandinavica, Section A – Animal Science 66, 177-82
T Byrne, B Santos, P Amer et al (2016), “New breeding objectives and selection indexes for the Australian dairy industry”, Journal of Dairy Science 99, 8146-67
D Martin-Collado, T Byrne, P Amer et al (2015), “Analyzing hidden patterns of farmers’ preferences for farm performance characteristics that may be related to tail-docking practice decisions”, NZSAP 2015 Conference, Dunedin, New Zealand, Proceedings of the New Zealand Society of Animal Production 75, 205-9
D Martin-Collado, T Byrne, P Amer et al (2015), “Analyzing the heterogeneity of farmers’ preferences for improvements in dairy cow traits using farmer typologies”, Journal of Dairy Science 96, 4148-61

J Kerslake, T Byrne , M Behrent, G MacLennan & D Martin-Collado (2015), “The reasons farmers choose to dock lamb tails to certain lengths, or leave them intact”, NZSAP 2015 Conference, Dunedin, New Zealand, Proceedings of the New Zealand Society of Animal Production 75, 210-14
K Smith & P Fennessy (2014), “Utilizing conjoint analysis to develop breeding objectives for the improvement of pasture species for contrasting environments when the relative values of individual traits are difficult to assess”, Sustainable Agriculture Research 3, 44-55
K Smith, C Ludemann, C Lewis et al(2014), “Estimating the value of genetic gain in perennial pastures with emphasis on temperate species”, Crop & Pasture Science 65, 1230-37
H Nielsen, P Amer & T Byrne (2014), “Approaches to formulating practical breeding objectives for animal production systems”, Acta Agriculturae Scandinavica, Section A – Animal Science 64, 2-12
T Byrne, P Amer, P Fennessy, P Hansen & B Wickham (2012), “A preference-based approach to deriving breeding objectives – applied to sheep breeding”, Animal 6, 778-88
K Smith & P Fennessy (2011), “The use of conjoint analysis to determine the relative importance of specific traits as selection criteria for the improvement of perennial pasture species in Australia”, Crop & Pasture Science 62, 355-65
T Byrne, P Fennessy, K Smith, P Hansen & P Amer (2011), “Preference-based approaches to deriving breeding objectives: Application to sheep and plant breeding”, AAABG 19^th Conference, Perth, Australia, Proceedings of the Association for the Advancement of Animal Breeding & Genetics 19, 35-38 News »

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Policing

T Sullivan, J Smith, F Ombler & H Brayley-Morris (2020), “Prioritizing the investigation of organized crime”, Policing and Society 30, 327-48

Retirement income policy

J Au, A Coleman & T Sullivan (2019), “When I’m 64: What do New Zealanders want in a retirement income policy?”, Agenda 26, 23-47
A Coleman (2016), “What do New Zealanders want from their retirement income scheme?”, EcoNZ@Otago 36, 3-5
J Au, A Coleman & T Sullivan (2015), “A practical approach to well-being based policy development: What do New Zealanders want from their retirement income policies?”, New Zealand Treasury Working Papers 15/14 (New Zealand Treasury, Annual Award for the Most Outstanding Working Paper 2015)

Robotics design

L Dalgaard (2014), “Technology assessment in robotic systems design using PAPRIKA”, Proceedings of the 7th International Conference on Human Systems Interaction, Lisbon, Portugal, 2014
L Dalgaard, T Heikkilä & J Koskinen (2014), “The R3-COP decision support framework for autonomous robotic system design”, Proceedings of the joint 45th International Symposium on Robotics and 8th German Conference on Robotics (ISR-Robotik 2014), München, Germany, 2014
T Heikkilä, L Dalgaard & J Koskinen (2013), “Designing Autonomous Robot Systems – Evaluation of the R3-COP Decision Support System approach”, Proceedings of the ERCIM/EWICS Workshop on Dependable Embedded and Cyber-physical Systems (DECS’13) at the 32nd International Conference on Computer Safety, Reliability & Security (SAFECOMP 2013), Toulouse, France, 2013
T Heikkilä, L Dalgaard & J Koskinen (2013), “Decision support for designing autonomous robot systems”, Proceedings of AutomaatioXX. Automation and Systems without Borders – Beyond Future, Helsinki, Finland, 2013

Sustainable building and engineering

S Mirzaee, D Fannon & M Ruth (2019), “A comparison of preference elicitation methods for multi-criteria design decisions about resilient and sustainable buildings”, Environment Systems and Decisions 39, 439-53
A Botici, V Ungureanu, A Ciutina et al (2014), “Sustainably challenges of residential reinforced-concrete panel buildings”, Urbanism. Arhitectură. Construcţii 5, 83-98

Tourism

J Romão, K Machino, & P Nijkamp (2017), “Assessment of wellness tourism development in Hokkaido: a multicriteria and strategic choice analysis”, Asia-Pacific Journal of Regional Science 1, 265-90

Transportation

M Titko, “Impacts of threats on the functionality of the transport critical infrastructure”, Proceedings of 22^nd International Scientific Conference, Transport Means, Trakai, Lithuania, 2018
M Miller & D Gransberg (2017), “Measuring users’ impact to support economic growth through transportation asset management planning”, International Journal of Public Policy 14, 1-26

Urban planning

F Moura, P Cambra & A Gonçalves (2017), “Measuring walkability for distinct pedestrian groups with a participatory assessment method: A case study in Lisbon”, Landscape and Urban Planning 157, 282-96
H Ji-yeon (2014), “The systematization of waste landfill site selection process utilizing GIS”, Korea Geospatial Information Science 22, 21-30
A Harding, “Anticipating future urban forms with restricted transport fuel availability: Location preferences of out-of-centre businesses in the Wellington region”, Working Paper, 2012
A Christofferson, “Housing choice in Dunedin”, City Planning, District Plan Monitoring Series, Research Report 2007/1, Dunedin City Council, 2007

Waste Management

MC Carnero (2020), “Waste segregation FMEA model integrating intuitionistic fuzzy set and the PAPRIKA method”, Mathematics 8, 1375
L Makarichi, K Techato & W Jutidamrongphan (2018), “Material flow analysis as a support tool for multi-criteria analysis in solid waste management decision-making”, Resources, Conservation and Recycling 139, 351-65
S-Y Chang & F Gronwald (2016), “A multi-criteria evaluation of the methods for recycling scrap tires”, The Journal of Solid Waste Technology and Management 42, 145-56

Wellbeing measurement

N Deyshappriya & S Feeny (2021), “Weighting the Dimensions of the Multidimensional Poverty Index: Findings from Sri Lanka”, Social Indicators Research, 1-19, early online version

Want to use 1000minds?

We support academics and students with discounted academic licenses and academic awards. And see potential research ideas if you’re looking for inspiration.

Please contact us if you’d like to use 1000minds for teaching – and we can easily send you a dedicated URL for your students to quickly enrol for their own user accounts so they can use 1000minds (e.g. class project).

Research citation

To mention 1000minds in your research, we recommend a reference like this:

1000minds (2020), 1000minds decision-making and conjoint analysis software, https://www.1000minds.com.

The reference for the article explaining the PAPRIKA method underpinning 1000minds is:

P Hansen & F Ombler (2008), “A new method for scoring multi-attribute value models using pairwise rankings of alternatives”, Journal of Multi-Criteria Decision Analysis 15, 87-107.