A Survey on Volunteer Management Systems

Voluntary work is indispensable in nearly every area of today's society, e.g., service activities in health care or emergencies. Not least because of this omnipresence of volunteering, already a plethora of volunteer management systems (VMS) has emerged, trying to support diverse volunteering processes and to deal with the broad spectrum and peculiarities of voluntary work. Thus, an in-depth understanding of functional commonalities and differences of VMS is urgently needed. The goal of this paper is therefore to provide an in-depth survey of existing VMS. For this, first, an initial attempt towards a reference model (RM) for VMS is presented, capturing their basic functional ingredients and interrelationships in terms of UML class diagrams. Second, the RM is operationalized by means of a set of evaluation criteria used to compare seven carefully selected VMS, thereby discussing their peculiarities and shortcomings. Third, lessons learned are provided together with research directions for future VMS.

[1]  Bin Zhu,et al.  Skill ontology-based semantic model and its matching algorithm , 2006, 2006 7th International Conference on Computer-Aided Industrial Design and Conceptual Design.

[2]  Douglas C. Derrick,et al.  Individual Differences that Predict Interactions in Mixed-Initiative Teams , 2015, 2015 48th Hawaii International Conference on System Sciences.

[3]  Frances M. T. Brazier,et al.  A Meta-Meta-Model for Seven Business Process Modeling Languages , 2013, 2013 IEEE 15th Conference on Business Informatics.

[4]  Mark Snyder,et al.  The Motivations to Volunteer , 1999 .

[5]  Kevin Crowston,et al.  The interdisciplinary study of coordination , 1994, CSUR.

[6]  R. Dale Safrit,et al.  Management Implications of Contemporary Trends in Volunteerism in the United States and Canada. , 2002 .

[7]  Ralf Heese,et al.  Ontology based Recruitment Process , 2004, GI Jahrestagung.

[8]  S. White,et al.  Volunteering in the United States, 2005 , 2006 .

[9]  Werner Retschitzegger,et al.  Logic-Based Modeling Approaches for Qualitative and Hybrid Reasoning in Dynamic Spatial Systems , 2015, ACM Comput. Surv..

[10]  Werner Retschitzegger,et al.  Towards Intelligent Support of Workflows , 2000 .

[11]  Imran Ghani,et al.  Ontology Matching Approaches for eRecruitment , 2012 .

[12]  Michael S. Bernstein,et al.  The future of crowd work , 2013, CSCW.

[13]  Christopher Cunningham,et al.  Gamification by Design - Implementing Game Mechanics in Web and Mobile Apps , 2011 .

[14]  Gert-Jan de Vreede,et al.  A Theoretical Model of User Engagement in Crowdsourcing , 2013, CRIWG.

[15]  Marta Indulska,et al.  Modeling languages for business processes and business rules: A representational analysis , 2009, Inf. Syst..

[16]  Michael S. Bernstein,et al.  Micro-volunteering: helping the helpers in development , 2013, CSCW '13.

[17]  Gerti Kappel,et al.  Coordination in Workflow Management Systems - A Rule-Based Approach , 1996, Coordination Technology for Collaborative Applications.

[18]  Alimohammad Shahri,et al.  Gamification for Volunteer Cloud Computing , 2014, 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing.

[19]  Peter Devereux 2015 State of the World's Volunteerism Report , 2015 .

[20]  Werner Retschitzegger,et al.  Towards a Reference Model for Social User Profiles: Concept & Implementation , 2011 .

[21]  Khaled Ghédira,et al.  Competency Models: A Review of Initiatives , 2012, 2012 IEEE 12th International Conference on Advanced Learning Technologies.

[22]  Luís Ferreira Pires,et al.  A Conceptual Model for the Development of CSCW Systems , 2000, COOP.

[23]  Elfriede Furtmueller-Ettinger Using technology for global recruitment: why HR/OB scholars need US knowledge? , 2012 .

[24]  Werner Retschitzegger,et al.  JobOlize - Headhunting by Information Extraction in the Era of Web 2.0 , 2008, IWWOST@ICWE.

[25]  Michael S. Bernstein,et al.  Soylent: a word processor with a crowd inside , 2010, UIST.

[26]  Xiaodong Wang,et al.  Dynamics in Hierarchical CSCW Systems , 2003, APPT.

[27]  Scott R. Klemmer,et al.  Shepherding the crowd yields better work , 2012, CSCW.

[28]  Gerti Kappel,et al.  A survey on UML-based aspect-oriented design modeling , 2011, CSUR.

[29]  Mark S. Fox,et al.  Semantic Matchmaking for Job Recruitment: An Ontology-Based Hybrid Approach , 2009 .

[30]  James F. Allen Maintaining knowledge about temporal intervals , 1983, CACM.

[31]  Elisa Bertino,et al.  Quality Control in Crowdsourcing Systems: Issues and Directions , 2013, IEEE Internet Computing.

[32]  Francesco M. Donini,et al.  Semantic-based Skill Management for Automated Task Assignment and Courseware Composition , 2007, J. Univers. Comput. Sci..

[33]  Frode Eika Sandnes,et al.  Finding Suitable Candidates: The Design of a Mobile Volunteering Matching System , 2011, HCI.

[34]  Boris Brandherm,et al.  Gumo - The General User Model Ontology , 2005, User Modeling.

[35]  Richard N. Taylor,et al.  Flexible Social Workflows: Collaborations as Human Architecture , 2012, IEEE Internet Computing.

[36]  Liz Sonenberg,et al.  A Composite Task Meta-model as a Reference Model , 2010, HCIS.

[37]  Chris Callison-Burch,et al.  Fast, Cheap, and Creative: Evaluating Translation Quality Using Amazon’s Mechanical Turk , 2009, EMNLP.

[38]  Jayne Cravens Internet-mediated Volunteering in the EU: Its history, prevalence, and approaches and how it relates to employability and social inclusion , 2014 .