Journal of Modelling in Management

Aims & Scope

Journal of Modelling in Management (JM2) provides a forum for academics and researchers with a strong interest in business and management modelling.

The journal analyses the conceptual antecedents and theoretical underpinnings leading to research modelling processes which derive useful consequences in terms of management science, business and management implementation and applications.

JM2 is focused on the utilization of management data, which is amenable to research modelling processes, and welcomes academic papers that not only encompass the whole research process (from conceptualization to managerial implications) but also make explicit the individual links between 'antecedents and modelling' (how to tackle certain problems) and 'modelling and consequences' (how to apply the models and draw appropriate conclusions).

The journal is particularly interested in innovative methodological and statistical modelling processes and those models that result in clear and justified managerial decisions.

JM2 specifically promotes and supports research writing, that engages in an academically rigorous manner, in areas related to research modelling such as: A priori theorizing conceptual models, Artificial intelligence, machine learning, Association rule mining, clustering, feature selection, Business analytics: Descriptive, Predictive, and Prescriptive Analytics, Causal analytics: structural equation modeling, partial least squares modeling, Computable general equilibrium models, Computer-based models, Data mining, data analytics with big data, Decision support systems and business intelligence, Econometric models, Fuzzy logic modeling, Generalized linear models, Multi-attribute decision-making models, Non-linear models, Optimization, Simulation models, Statistical decision models, Statistical inference making and probabilistic modeling, Text mining, web mining, and visual analytics, Uncertainty-based reasoning models.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2.2
2024	1.80

Journal Rank

Year	Value
2024	10756

Journal Citation Indicator

Year	Value
2024	674

SJR (SCImago Journal Rank)

Year	Value
2024	0.541

Quartile

Year	Value
2024	Q2

h-index

Year	Value
2024	43

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Business, Management and Accounting and Decision Sciences, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Technology acceptance: a meta‐analysis of the TAM: Part 1

  Citation: 376

  Authors: Shumaila Y., Gordon R., John G.

• The error term in formative measurement models: interpretation and modeling implications

  Citation: 224

  Authors: Adamantios

• Technology acceptance: a meta‐analysis of the TAM: Part 2

  Citation: 186

  Authors: Shumaila Y., Gordon R., John G.

• Analysis of barriers in implementation of digital transformation of supply chain using interpretive structural modelling approach

  Citation: 168

  Authors: Prakash, Rakesh, Inayat

• A review of recent approaches for capturing heterogeneity in partial least squares path modelling

  Citation: 131

  Authors: Marko

• Pre-owned housing price index forecasts using Gaussian process regressions

  Citation: 125

  Authors: Bingzi, Xiaojie

• Selection of lean manufacturing systems using the PROMETHEE

  Citation: 114

  Authors: G., Rambabu

• Development of an Industry 4.0 maturity model for the delivery process in supply chains

  Citation: 101

  Authors: Björn, Vanessa

• Integrating knowledge management (KM) strategies and processes to enhance organizational creativity and performance

  Citation: 98

  Authors: Khuram, Sami Ullah, Ahmed Faisal Imtiaz, Farhan, Ali

• Thermal coal futures trading volume predictions through the neural network

  Citation: 96

  Authors: Bingzi, Xiaojie, Yun

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