The Journal of Choice Modelling publishes theoretical and applied papers in the field of choice modelling. Papers are expected to either make a methodological contribution to the field, or to present an innovative application. The journal is not limited to one area of study, such as transport or marketing, but invites contributions from across a range of disciplines where the analysis of choice behaviour is a topic of interest. While the majority of papers focus on the use of discrete choice models, contributions looking at other methods are also welcome. Similarly, the Journal of Choice Modelling also welcomes contributions looking at survey design.
In addition to standard full length research papers, JOCM also welcomes four other types of submissions:
These are shorter articles that can be technical notes addressing a specific model specification, survey design, data collection or estimation issue or discussion pieces highlighting a particular concern in applied work. No specific length limit is imposed, but potential authors may wish to look at volume 21 as an example for such articles.
Research notes are subject to the normal blind refereeing process to maintain the high standards of the journal.
These are papers presenting software for choice model estimation and/or application as well as packages for survey design and data collection. These need to be substantial pieces of software that either improve on existing tools available, emulate them in a different environment and are likely to lead to widespread use. This type of paper is meant to provide readers with new tools for their work, rather than serve as a marketing device. The expectation is that the vast majority of papers submitted will report on free (open access) software. While we do not rule out papers discussing commercial software, an explicit case will have to be made to the editors as to why the article is of interest to the broad JOCM readership.
Software papers will be reviewed initially by associate editors who may additionally rely on the advice of other experts in the field.
These are either papers reporting on innovative data collection efforts or papers describing datasets that can be used for model benchmarking. For innovative data collection papers, there is no requirement to make the data publicly available (although it is preferred) as long as the paper provides useful insights for other studies. For benchmark datasets, public access is required. Datasets can be deposited on the JOCM website, existing data repositories and/or websites administered by the authors. Long term maintenance of the sites should be guaranteed. Data papers should discuss the survey approach taken, highlight any novel elements, and in the case of data made publicly available, should include an online data dictionary.
Data papers will be reviewed initially by associate editors who may additionally rely on the advice of other experts in the field.
For data papers to be considered for publication, authors need to show that their work is relevant beyond their own topic area.
These are full length articles that review the literature in a specific area of research within the scope of JOCM. While literature review papers are not expected to include new research, they should provide new knowledge or thinking in terms of providing insightful overviews or critiques of existing work and highlighting gaps in that work. Cross- disciplinary review papers are especially welcome.
Review papers are subject to the normal blind refereeing process to maintain the high standards of the journal.
These are full length articles that discuss current issues in choice modelling, set research agendas, or provide an outlook for the field. Cross-disciplinary review papers are especially welcome.
|Issue volume||Issue year||Planned ship date||Actual ship date|
|38C||2021||Feb 25, 2021||Feb 24, 2021|
|39C||2021||Jun 10, 2021||Jun 09, 2021|
|40C||2021||Aug 26, 2021||Aug 18, 2021|
|41C||2021||Nov 25, 2021|
|42C||2022||Mar 08, 2022|
|43C||2022||Jun 10, 2022|
|44C||2022||Sep 05, 2022|
|45C||2022||Dec 05, 2022|