Example of Journal of Behavioral Decision Making format

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Journal of Behavioral Decision Making — Template for authors

Publisher: Wiley

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Guideline source

Categories	Rank	Trend in last 3 yrs
Sociology and Political Science	#142 of 1269	up by 18 ranks
Arts and Humanities (miscellaneous)	#56 of 306	up by 18 ranks
Strategy and Management	#118 of 440	down by 29 ranks
Applied Psychology	#75 of 227	up by 3 ranks
Decision Sciences (all)	#16 of 37	down by 1 rank

Journal quality:

High

Last 4 years overview: 234 Published Papers | 902 Citations

Indexed in: Scopus

Last updated: 09/06/2020

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Journal Performance & Insights

Impact Factor

CiteRatio

Determines the importance of a journal by taking a measure of frequency with which the average article in a journal has been cited in a particular year.

A measure of average citations received per peer-reviewed paper published in the journal.

1.715

4% from 2018

Impact factor for Journal of Behavioral Decision Making from 2016 - 2019
Year	Value
2019	1.715
2018	1.791
2017	1.788
2016	1.738

Graph view

3.9

3% from 2019

CiteRatio for Journal of Behavioral Decision Making from 2016 - 2020
Year	Value
2020	3.9
2019	4.0
2018	3.2
2017	3.1
2016	5.8

Graph view

Insights

Impact factor of this journal has decreased by 4% in last year.
This journal’s impact factor is in the top 10 percentile category.

Insights

CiteRatio of this journal has decreased by 3% in last years.
This journal’s CiteRatio is in the top 10 percentile category.

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

Measures weighted citations received by the journal. Citation weighting depends on the categories and prestige of the citing journal.

Measures actual citations received relative to citations expected for the journal's category.

1.136

14% from 2019

SJR for Journal of Behavioral Decision Making from 2016 - 2020
Year	Value
2020	1.136
2019	1.315
2018	1.284
2017	0.982
2016	1.759

Graph view

1.221

2% from 2019

SNIP for Journal of Behavioral Decision Making from 2016 - 2020
Year	Value
2020	1.221
2019	1.245
2018	0.972
2017	1.049
2016	1.691

Graph view

Insights

SJR of this journal has decreased by 14% in last years.
This journal’s SJR is in the top 10 percentile category.

Insights

SNIP of this journal has decreased by 2% in last years.
This journal’s SNIP is in the top 10 percentile category.

Journal of Behavioral Decision Making

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Wiley

Journal of Behavioral Decision Making

The Journal of Behavioral Decision Making is a multidisciplinary journal with a broad base of content and style. It publishes original empirical reports, critical review papers, theoretical analyses and methodological contributions. The Journal also features book, software and decision aiding technique reviews, abstracts of important articles published elsewhere and teaching suggestions. The objective of the Journal is to present and stimulate behavioral research on decision making and to provide a forum for the evaluation of complementary, contrasting and conflicting perspectives. These perspectives include psychology, management science, sociology, political science and economics. Studies of behavioral decision making in naturalistic and applied settings are encouraged. Articles on specialist topics aim for wider readability by including fully referenced introductions on the background to a particular study and, where possible, discuss the broader implications of the work. The Journal especially welcomes manuscripts that deal with issues that have cross-disciplinary impact. Such manuscripts will be considered for publication together with a selection of commentaries from members of the editorial board. Reviews are "collaborationist" in the sense that reviewers are asked to do more than point out flaws and will suggest design improvements, new ideas, relevant references and follow-up studies. Reviewers are offered the opportunity to publish commentaries alongside the article. Papers published in the Journal of Behavioral Decision Making will encompass, but not be limited to, the following areas: • individual decision making • management science and decision aids, emphasizing the role of judgment and behavioral factors in the implementation of decision technologies • interpersonal and small group decision making • organizational decision making • strategic decision making • social and political structuring of decision making, providing a broad account of the social context and its constraints on decision making • behavioral economics • expert systems, emphasizing the modeling of judgment and behavioral issues in evaluation and implementation • consumer decision making • behavioral accounting • medical and clinical decision making Read Less

Sociology and Political Science

Arts and Humanities (miscellaneous)

Strategy and Management

Applied Psychology

General Decision Sciences

Social Sciences

Last updated on	08 Jun 2020
ISSN	0894-3257
Impact Factor	High - 1.578
Open Access	Yes
Sherpa RoMEO Archiving Policy	Yellow
Plagiarism Check	Available via Turnitin
Endnote Style	Download Available
Bibliography Name	apa
Citation Type	Numbered [25]
Bibliography Example	Beenakker, C.W.J. (2006) Specular andreev reflection in graphene.Phys. Rev. Lett., 97 (6), 067 007. URL 10.1103/PhysRevLett.97.067007.

Top papers written in this journal

Journal Article • DOI: 10.1002/(SICI)1099-0771(199909)12:3<183::AID-BDM318>3.0.CO;2-F •

Mental accounting matters

Richard H. Thaler¹ •

01 Sep 1999 - Journal of Behavioral Decision Making

Abstract:

Mental accounting is the set of cognitive operations used by individuals and households to organize, evaluate, and keep track of financial activities Making use of research on this topic over the past decade, this paper summarizes the current state of our knowledge about how people engage in mental accounting activities Three... Mental accounting is the set of cognitive operations used by individuals and households to organize, evaluate, and keep track of financial activities Making use of research on this topic over the past decade, this paper summarizes the current state of our knowledge about how people engage in mental accounting activities Three components of mental accounting receive the most attention This first captures how outcomes are perceived and experienced, and how decisions are made and subsequently evaluated The accounting system provides the inputs to be both ex ante and ex post cost–benefit analyses A second component of mental accounting involves the assignment of activities to specific accounts Both the sources and uses of funds are labeled in real as well as in mental accounting systems Expenditures are grouped into categories (housing, food, etc) and spending is sometimes constrained by implicit or explicit budgets The third component of mental accounting concerns the frequency with which accounts are evaluated and ‘choice bracketing’ Accounts can be balanced daily, weekly, yearly, and so on, and can be defined narrowly or broadly Each of the components of mental accounting violates the economic principle of fungibility As a result, mental accounting influences choice, that is, it matters Copyright © 1999 John Wiley & Sons, Ltd read more

Topics:

Positive accounting (71%)71% related to the paper, Accounting information system (69%)69% related to the paper, National accounts (66%)66% related to the paper,

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2,943 Citations

Open access • Journal Article • DOI: 10.1002/(SICI)1099-0771(200001/03)13:1<1::AID-BDM333>3.0.CO;2-S •

The affect heuristic in judgments of risks and benefits

Melissa L. Finucane, Ali Siddiq Alhakami¹, •

01 Jan 2000 - Journal of Behavioral Decision Making

Abstract:

This paper re-examines the commonly observed inverse relationship between perceived risk and perceived benefit. We propose that this relationship occurs because people rely on affect when judging the risk and benefit of specific hazards. Evidence supporting this proposal is obtained in two experimental studies. Study 1 invest... This paper re-examines the commonly observed inverse relationship between perceived risk and perceived benefit. We propose that this relationship occurs because people rely on affect when judging the risk and benefit of specific hazards. Evidence supporting this proposal is obtained in two experimental studies. Study 1 investigated the inverse relationship between risk and benefit judgments under a time-pressure condition designed to limit the use of analytic thought and enhance the reliance on affect. As expected, the inverse relationship was strengthened when time pressure was introduced. Study 2 tested and confirmed the hypothesis that providing information designed to alter the favorability of one's overall affective evaluation of an item (say nuclear power) would systematically change the risk and benefit judgments for that item. Both studies suggest that people seem prone to using an ‘affect heuristic’ which improves judgmental efficiency by deriving both risk and benefit evaluations from a common source—affective reactions to the stimulus item. Copyright © 2000 John Wiley & Sons, Ltd. read more

Topics:

Affect heuristic (60%)60% related to the paper, Risk perception (53%)53% related to the paper, Affect (psychology) (50%)50% related to the paper

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2,525 Citations

Journal Article • DOI: 10.1002/BDM.414 •

A domain-specific risk-attitude scale: measuring risk perceptions and risk behaviors

Elke U. Weber¹, Ann Renée Blais, •

01 Oct 2002 - Journal of Behavioral Decision Making

Abstract:

We present a psychometric scale that assesses risk taking in five content domains: financial decisions (separately for investing versus gambling), health/safety, recreational, ethical, and social decisions. Respondents rate the likelihood that they would engage in domain-specific risky activities (Part I). An optional Part II... We present a psychometric scale that assesses risk taking in five content domains: financial decisions (separately for investing versus gambling), health/safety, recreational, ethical, and social decisions. Respondents rate the likelihood that they would engage in domain-specific risky activities (Part I). An optional Part II assesses respondents' perceptions of the magnitude of the risks and expected benefits of the activities judged in Part I. The scale's construct validity and consistency is evaluated for a sample of American undergraduate students. As expected, respondents' degree of risk taking was highly domain-specific, i.e. not consistently risk-averse or consistently risk-seeking across all content domains. Women appeared to be more risk-averse in all domains except social risk. A regression of risk taking (likelihood of engaging in the risky activity) on expected benefits and perceived risks suggests that gender and content domain differences in apparent risk taking are associated with differences in the perception of the activities' benefits and risk, rather than with differences in attitude towards perceived risk. Copyright © 2002 John Wiley & Sons, Ltd. read more

Topics:

Specific risk (61%)61% related to the paper, Risk perception (58%)58% related to the paper

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1,799 Citations

Journal Article • DOI: 10.1002/BDM.1753 •

Data collection in a flat world: the strengths and weaknesses of mechanical turk samples

Joseph K. Goodman¹, Cynthia Cryder¹, •

01 Jul 2013 - Journal of Behavioral Decision Making

Abstract:

Mechanical Turk (MTurk), an online labor system run by Amazon.com, provides quick, easy, and inexpensive access to online research participants. As use of MTurk has grown, so have questions from behavioral researchers about its participants, reliability, and low compensation. In this article, we review recent research about M... Mechanical Turk (MTurk), an online labor system run by Amazon.com, provides quick, easy, and inexpensive access to online research participants. As use of MTurk has grown, so have questions from behavioral researchers about its participants, reliability, and low compensation. In this article, we review recent research about MTurk and compare MTurk participants with community and student samples on a set of personality dimensions and classic decision-making biases. Across two studies, we find many similarities between MTurk participants and traditional samples, but we also find important differences. For instance, MTurk participants are less likely to pay attention to experimental materials, reducing statistical power. They are more likely to use the Internet to find answers, even with no incentive for correct responses. MTurk participants have attitudes about money that are different from a community sample’s attitudes but similar to students’ attitudes. Finally, MTurk participants are less extraverted and have lower self-esteem than other participants, presenting challenges for some research domains. Despite these differences, MTurk participants produce reliable results consistent with standard decision-making biases: they are present biased, risk-averse for gains, risk-seeking for losses, show delay/expedite asymmetries, and show the certainty effect—with almost no significant differences in effect sizes from other samples. We conclude that MTurk offers a highly valuable opportunity for data collection and recommend that researchers using MTurk (1) include screening questions that gauge attention and language comprehension; (2) avoid questions with factual answers; and (3) consider how individual differences in financial and social domains may influence results. Copyright © 2012 John Wiley & Sons, Ltd. read more

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1,755 Citations

Open access • Journal Article • DOI: 10.1002/BDM.515 •

Portrait of The Angry Decision Maker: How Appraisal Tendencies Shape Anger's Influence on Cognition.

Jennifer S. Lerner¹, Larissa Z. Tiedens² •

01 Apr 2006 - Journal of Behavioral Decision Making

Abstract:

This paper reviews the impact of anger on judgment and decision making. Section I proposes that anger merits special attention in the study of judgment and decision making because the effects of anger often diverge from those of other negative emotions. Section II presents an Appraisal-Tendency Framework for predicting and or... This paper reviews the impact of anger on judgment and decision making. Section I proposes that anger merits special attention in the study of judgment and decision making because the effects of anger often diverge from those of other negative emotions. Section II presents an Appraisal-Tendency Framework for predicting and organizing such effects. Section III reviews empirical evidence for the uniqueness of anger’s relations to judgment and decision making. Section IV connects the Appraisal-Tendency Framework to associated mechanisms and theories. Drawing on the evidence, Section V presents the question of whether anger should be considered a positive emotion. It also proposes the hypothesis that anger will be experienced as relatively unpleasant and unrewarding when reflecting back on the source of one’s anger but experienced as relatively pleasant and rewarding when looking forward. Section VI synthesizes the evidence into a new portrait of the angry decision maker. Copyright # 2006 John Wiley & Sons, Ltd. read more

Topics:

Anger (61%)61% related to the paper

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847 Citations

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13. What is Sherpa RoMEO Archiving Policy for Journal of Behavioral Decision Making?

We extracted this data from Sherpa Romeo to help researchers understand the access level of this journal in accordance with the Sherpa Romeo Archiving Policy for Journal of Behavioral Decision Making. The table below indicates the level of access a journal has as per Sherpa Romeo's archiving policy.

RoMEO Colour	Archiving policy
Green	Can archive pre-print and post-print or publisher's version/PDF
Blue	Can archive post-print (ie final draft post-refereeing) or publisher's version/PDF
Yellow	Can archive pre-print (ie pre-refereeing)
White	Archiving not formally supported

FYI:

Pre-prints as being the version of the paper before peer review and
Post-prints as being the version of the paper after peer-review, with revisions having been made.

14. What are the most common citation types In Journal of Behavioral Decision Making?

The 5 most common citation types in order of usage for Journal of Behavioral Decision Making are:.

S. No.	Citation Style Type
1.	Author Year
2.	Numbered
3.	Numbered (Superscripted)
4.	Author Year (Cited Pages)
5.	Footnote

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Related Journals

Journal Performance & Insights

Impact Factor

CiteRatio

1.715

3.9

Insights

Insights

SCImago Journal Rank (SJR)

Source Normalized Impact per Paper (SNIP)

1.136

1.221

Insights

Insights

Journal of Behavioral Decision Making

Journal of Behavioral Decision Making

Top papers written in this journal

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Topics:

Abstract:

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What to expect from SciSpace?

Speed and accuracy over MS Word

Plagiarism Reports via Turnitin

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Frequently asked questions

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Trusted by academicians

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