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Journal Basket: Supporting Cross-Disciplinary Literature Searching and Methodological Transparency

Michael Cahalane

arXiv:2610.30544v1cs.DLcs.IR

TL;DR

Business students must decide which scholarly sources and databases to search, but journal classifications and database organization make those choices difficult to construct and report. Journal Basket uses ABDC-based journal selections to generate database-specific searches and document the selected journals. Its checks do not establish retrieval completeness, and educational benefits remain un demonstrated.

  • Problem

    Students must identify relevant publications, distinguish disciplinary sources, construct database searches, and report review decisions across differently organized classification systems and databases.

    Source: §1 · pp. 1–2

  • Method

    Journal Basket combines ABDC-based journal selections across disciplines, generates searches for supported databases, and records the chosen journals and settings.

    Source: §1 · p. 3, §6 · p. 16

  • Results

    With default settings, 2,647 searchable journals require 29 Scopus parts, 47 Web of Science parts, and 119 Business Source parts in version 0.30.3.

    Source: §4.3 · p. 12

  • Takeaways & Limitations

    The workflow links explicit journal boundaries to database-specific queries and a record of search decisions for students learning to design literature reviews.

    Source: §6 · p. 16

    Journal baskets inherit the source list’s coverage and disciplinary assumptions, journal ratings do not assess individual articles, and reported checks do not measure retrieval completeness or demonstrate educational benefits.

    Source: §5.3 · p. 15

Abstract

from arXiv · show

Business students beginning a research project must learn how to identify relevant scholarly publications, distinguish disciplinary sources, construct database searches, and report the decisions behind their literature reviews. These tasks can be difficult because journal classification systems and bibliographic databases organize scholarly information differently. This paper introduces Journal Basket, a browser-based tool developed to help business students conduct journal-bounded literature searches and currently available in beta. Journal Basket enables users to construct journal collections from the Australian Business Deans Council (ABDC) Journal Quality List, choose from 42 business discipline presets, entire ABDC fields or the Financial Times FT50 list, apply journal rating thresholds, and generate search expressions for Scopus, Web of Science and Business Source. Users can also inspect and modify journal collections, download journal inventories, and generate methodological descriptions and deterministic identifiers for journal selections. Companion learning resources use explanations, examples and interactive activities to teach review methods, database choice, referencing and the use of contemporary AI tools. Drawing on the journal-basket concept used by Litbaskets.io, I describe the educational motivation, design rationale, technical architecture, and practical workflow of Journal Basket. I distinguish reproducing a journal collection from reproducing an entire database search and discuss the limitations of journal rankings as search boundaries. The paper addresses scholarly information retrieval through a workflow for defining journal boundaries, generating database-specific queries and documenting search decisions.

1. Introduction

Business students must choose databases, terms, publication types, and source boundaries before they can interpret a literature’s evidence. Journal Basket responds with an ABDC-based workflow for combining journal collections, generating database searches, and recording selections.

1. Introduction

  • Students may know their topic but not which journals publish relevant work, and disciplinary framings can lead to different journals and terminology.A journal-bounded search therefore captures a particular scholarly conversation, not necessarily the whole literature.Source: §1 · pp. 1–2
  • Tarafdar and Davison (2018) found that articles in the AIS Senior Scholars' Basket of Eight from 2010–2014 were predominantly intradisciplinary, raising questions about searches confined to familiar IS outlets.Source: §1 · p. 2
  • A journal basket sets the publication boundaries of a search, while keywords determine which articles within those boundaries are retrieved.Source: §1 · p. 2
  • Manual basket construction can require managing changing journal names, differing ISSNs, database-specific syntax, and queries too large to execute at once.This effort can arise before students have considered the methodological implications of their choices.Source: §1 · p. 2
  • Journal Basket takes inspiration from LitBaskets but uses the ABDC Journal Quality List to build and combine baskets across business disciplines.The ABDC list offers students a recognizable framework for identifying business publication outlets, especially for cross-field questions.Source: §1 · pp. 2–3
  • Journal Basket combines journal collections across business disciplines, translates selections into searches for three databases, and retains an inspectable record of journals and settings.The paper also describes teaching resources, documents a dated search execution, and identifies evaluation needed to assess educational and research benefits.Source: §1 · p. 3

2. Background and Methodological Motivation

Search design depends on review purpose, database coverage, and whether the researcher is exploring or applying a settled strategy. Journal baskets make source boundaries explicit, but their adequacy depends on the question and does not replace cross-disciplinary interpretation or justification.

2.1. From a research question to search terms

  • Search expressions combine alternative terms with OR and concept groups with AND, while the review’s purpose—not tool convenience—should determine the search strategy.A journal basket cannot compensate for relevant publications absent from the selected database, and exploratory searching may require revising both terms and journals as reading develops.Source: §2.1 · pp. 3–4
  • Journal Basket constructs database-specific source restrictions from the user’s journal selection and terms, while researchers develop the substantive concepts and synonyms.Database interfaces differ in their syntax and search restrictions.Source: §2.1 · pp. 3–4

2.2. Why use a journal basket?

  • A journal basket suits questions about a particular scholarly community, but it is purposeful sampling rather than a universal substitute for comprehensive searching.Its adequacy must be judged against the review objective; a broader evidence review may need sources beyond the selected journals and disciplines.Source: §2.2 · p. 4
  • Combining business disciplines brings their journals into one basket with duplicates removed, making newly eligible publication outlets inspectable against the research question.The expanded list can surface perspectives such as consumer behavior, trust, organizational strategy, and work practices.Source: §2.2 · pp. 4–5
  • The ABDC list organizes business journals by research field and A*, A, B, and C ratings, but ratings describe outlets rather than individual articles’ relevance or quality.Users can combine disciplines and rating categories, while remaining responsible for justifying their choices.Source: §2.2 · p. 5

2.3. Searching across disciplines and developing interdisciplinary knowledge

  • A broader search creates opportunities to encounter different perspectives, but comparing and integrating those perspectives remains the student’s responsibility.Journal Basket supports this search stage by letting users expand and inspect publication boundaries.Source: §2.3 · p. 5

2.4. Teaching the distinction between search boundaries and search content

  • The basket answers where to look and keywords answer what to look for, so students can distinguish source boundaries from search content.They must still justify selected disciplines and consider relevant work beyond the ABDC list, including conferences, books, and journals classified elsewhere.Source: §2.4 · pp. 5–6

3. Software Overview and Design Rationale

Journal Basket guides users from defining journal boundaries to generating database-specific searches and documenting their choices, with companion resources for review methods, AI use, and referencing. Its cross-disciplinary workflow builds on LitBaskets but uses ABDC-based collections and supports several databases.

3.1. User workflow

  • Search guides keyword, discipline, rating, and database choices; Journals lets users inspect the resulting basket, while Write-up supports documenting their decisions.The workflow makes these choices visible for discussion between students and supervisors.Source: §3.1 · p. 6
  • Users can select 42 presets, ABDC fields, or the FT50 list and combine disciplines, but presets remain drafts awaiting expert review.Combining collections can surface conversations less visible from a single disciplinary starting point.Source: §3.1 · pp. 6–7

3.2. Relationship to LitBaskets: design and functionality

  • LitBaskets groups IS-associated journals into progressively broader baskets, whereas Journal Basket builds collections from ABDC records and ratings and generates searches for Scopus, Web of Science, and Business Source.Journal Basket also supports a Write-up area for recording selected journals and search settings.Source: §3.2 · pp. 7–8

3.3. A worked student example

  • Combining discipline baskets changes the research scope; Journal Basket joins selected lists and removes duplicate journals so each included journal is searched once.The student can compare a narrow auditing basket with a broader one spanning auditing, accounting, and Information Systems.Source: §3.3 · p. 9
  • Selecting only A* and A journals narrows the basket, but higher ratings alone do not justify assuming that included articles are individually better.The student should state the purpose of the restriction and consider expanding the basket or supplementing it for broader coverage.Source: §3.3 · p. 9
  • The example’s search expression uses OR for alternatives, AND to link concepts, and truncation to potentially match word variants; database syntax should be checked and results refined.Journal Basket also flags common typing errors such as unmatched quotation marks or brackets.Source: §3.3 · p. 9
  • Journal Basket combines the basket’s journal restrictions with the terms in the selected database’s syntax and splits large baskets into smaller executable search components.The student runs and checks the search in the database, then records the complete expression, date, filters, and result count.Source: §3.3 · pp. 9–10

3.4. Learning resources for research students

  • The learning resources pair review-method guidance with AI-use and referencing instruction through explanations, worked examples, comparison tables, and interactive activities.They cover review approaches and database choice, testing AI-suggested terms and verifying claims, and practices such as citation tracing and DOI checks.Source: §3.4 · p. 10

4. Technical Architecture and Data Processing

Journal Basket prepares journal records, translates selected baskets into database-specific searches, and helps document the choices made. The paper reports implementation and search checks, while leaving educational and methodological effectiveness for future evaluation.

4.1. Browser-based implementation

  • The browser-based application requires no account, and its project documentation says its aggregate-count endpoint does not record search terms or user identities.Google Analytics is consent-controlled and designed to avoid transmitting search terms; development documentation also records 115 passing automated tests in version 0.30.3.Source: §4.1 · p. 11, §4.5 · p. 14

4.2. Journal records and identifier quality

  • Journal Basket checks journal identifiers and records data corrections; validation examples show that adding a corroborated identifier can change the number of Scopus records retrieved.The dataset includes former-title ISSNs for 58 journals and Scopus source-identifier matches for 2,095 journals.Source: §4.2 · p. 11

4.3. Generating executable searches

  • Selected journals are translated into each database’s source syntax: Scopus uses SRCID() or ISSN(), Web of Science uses IS=, and Business Source uses the IS field code.Large query sets are split into parts; Web of Science parts also observe a 2,250-character link limit, and researchers should check executed searches and combined results.Source: §4.3 · pp. 11–12
  • With short terms and default settings, 2,647 searchable journals require 29 Scopus parts, 47 Web of Science parts, and 119 Business Source parts.The figures are for version 0.30.3; the 219 A* journals require 3, 5, and 11 parts, respectively.Source: §4.3 · p. 12

4.4. Documenting a basket

  • The Write-up page drafts a methods paragraph from the selected database, journal criteria, search terms, and settings, and lists supporting references and an AI-check prompt.Researchers can adapt the draft to their actual procedures; a deterministic code identifies the ABDC edition and version and sorted journal identifiers, not search terms.Source: §4.4 · pp. 12–13

4.5. Development and verification

  • 24 generated-search cases passed their respective checks; 16 were executed through Scopus or Web of Science APIs, while eight Business Source cases checked link construction only.The executed query parts returned 12,458 records, of which 612 were checked for journal membership and specified restrictions.Source: §4.5 · p. 14

5. Applications, Implications, and Limitations

The tool supports comparing and documenting journal boundaries for student projects and experienced researchers. Its classification-based scope and unmeasured retrieval completeness and educational effects limit what can yet be concluded.

5.1. Student learning and supervision

  • Students can compare an Information Systems-only basket with broader selections and discuss the trade-off between focus and breadth for their research question.The tool also provides journal inventories and draft descriptions that can support discussion of selected disciplines, ratings, exclusions, and supplementary searches.Source: §5.1 · p. 14

5.2. Use by experienced researchers

  • Experienced researchers can adjust an ABDC-based selection, move from one discipline to a combined basket, and retain or export a record of journals searched.For interdisciplinary reviews, they may add adjacent fields when their question calls for perspectives developed elsewhere.Source: §5.2 · p. 15
  • Journal baskets do not assess article quality, resolve theoretical disagreements, screen or synthesize findings, or replace other relevant sources such as books and conference proceedings.Source: §5.2 · p. 15

5.3. Limitations and future evaluation

  • A classification-based basket reflects its source list and curated presets, journal ratings do not establish individual-article relevance or quality, and database indexing or identifier histories may limit retrieval.Source: §5.3 · p. 15
  • Journal classification cannot establish every article’s intellectual orientation, and a combined business basket may still miss relevant scholarship beyond business.Journal selection should be justified against the review question and supplemented where necessary.Source: §5.3 · p. 15
  • The reported checks do not measure retrieval completeness; educational and productivity benefits remain intended outcomes pending further evaluation.Proposed studies would compare generated searches with verified references and observe novice students constructing and explaining searches.Source: §5.3 · p. 15

6. Conclusion

Journal Basket links journal-boundary choices to database-specific queries and a record of search decisions. The workflow is intended for business students learning review design and also offers experienced researchers a way to document searches across business disciplines.

6. Conclusion

  • Journal Basket uses the ABDC Journal Quality List to support basket selection across business disciplines, building on the AIS Basket of Eight tradition and LitBaskets’ broader exploratory approach.Source: §6 · p. 16
  • Users can combine disciplines, generate searches for Scopus, Web of Science, and Business Source, and retain a record of journal choices.Source: §6 · p. 16
  • The workflow is particularly relevant to business students learning how a literature review is designed and can also serve experienced researchers drawing on several business disciplines.Source: §6 · p. 16

Software Availability

Journal Basket is available as a beta application; the paper describes version 0.30.3 dated 22 October 2026.

Software Availability

Declaration of Generative AI Use

The author used ChatGPT and Claude to assist with drafting and editing the manuscript, then reviewed and revised the AI-assisted content and retained responsibility for it.

Declaration of Generative AI Use

Appendix A. Recorded search execution

The appendix records a Scopus API search using two A-rated journals in the Management Accounting preset and the phrase “corporate governance”; it is an implementation example, not a completed literature review.

Appendix A. Recorded search execution

  • The Scopus API search returned 14 records, all checked for membership in the selected journals.The count reflects the execution date and may change as Scopus is updated.Source: Appendix A · p. 18
  • The search used the two A-rated specialist journals in the Management Accounting preset.The complete inventory is identified as Table A1.Source: Appendix A · p. 18, Table A1 · p. 18
  • The query searched “corporate governance” in titles, abstracts, and keywords, without year or document-type restrictions.Source: Appendix A · p. 18
  • The recorded search is an implementation example, not a completed literature review.Source: Appendix A · p. 18
  • The recorded Scopus expression is TITLE-ABS-KEY("corporate governance") AND (SRCID(4100151511) OR SRCID(19900192221)).The query uses Scopus source identifiers rather than ISSNs; its journal list code is b8872e8ee046.Source: Appendix A · p. 18
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