INDUSTRIAL MANAGEMENT & DATA SYSTEMS

Scope & Guideline

Unlocking Potential with Advanced Research in Industrial Systems

Introduction

Delve into the academic richness of INDUSTRIAL MANAGEMENT & DATA SYSTEMS with our guidelines, detailing its aims and scope. Our resource identifies emerging and trending topics paving the way for new academic progress. We also provide insights into declining or waning topics, helping you stay informed about changing research landscapes. Evaluate highly cited topics and recent publications within these guidelines to align your work with influential scholarly trends.
LanguageEnglish
ISSN0263-5577
PublisherEMERALD GROUP PUBLISHING LTD
Support Open AccessNo
CountryUnited Kingdom
TypeJournal
Convergefrom 1980 to 2024
AbbreviationIND MANAGE DATA SYST / Ind. Manage. Data Syst.
Frequency9 issues/year
Time To First Decision-
Time To Acceptance-
Acceptance Rate-
Home Page-
AddressFloor 5, Northspring 21-23 Wellington Street, Leeds, W YORKSHIRE LS1 4DL, ENGLAND

Aims and Scopes

The journal 'Industrial Management & Data Systems' focuses on the intersection of management practices and data systems, emphasizing the integration of digital technologies in industrial contexts. It aims to disseminate innovative research that enhances operational efficiency and decision-making in the face of rapid technological advancements.
  1. Digital Transformation in Supply Chains:
    Research aimed at understanding how digital technologies, such as big data analytics, artificial intelligence, and blockchain, can enhance supply chain operations and transparency.
  2. Sustainability and Green Practices:
    Exploration of sustainable supply chain management practices, innovations in environmental performance, and the implementation of circular economy principles within industrial contexts.
  3. Impact of Social Media and E-Commerce:
    Investigation into the roles of social media and e-commerce platforms in shaping consumer behavior, marketing strategies, and operational efficiencies in various industries.
  4. Data-Driven Decision Making:
    Research focusing on leveraging data analytics and modeling techniques to improve decision-making processes across various business functions, including finance, operations, and marketing.
  5. Innovation and Technology Management:
    Studies that explore the management of technological innovations, including the adoption and integration of new technologies within organizations to enhance competitive advantage.
The journal has witnessed a significant shift in focus towards emerging themes that reflect current trends in technology and management practices. These themes are indicative of the evolving landscape of industrial management and data systems.
  1. Artificial Intelligence and Machine Learning:
    There is an increasing focus on the applications of AI and machine learning in various sectors, examining their impact on operational efficiency and decision-making processes.
  2. Blockchain and Supply Chain Transparency:
    Research into blockchain technology's role in enhancing transparency and trust within supply chains is gaining momentum, reflecting its importance in modern industrial practices.
  3. Social Commerce and Consumer Engagement:
    The integration of social media with commerce, particularly in live streaming and influencer marketing, is emerging as a key area of interest, highlighting new consumer engagement strategies.
  4. Resilience and Risk Management in Supply Chains:
    Post-pandemic studies focusing on building resilience and managing risks in supply chains are trending, as organizations seek to adapt to disruptions and uncertainties.
  5. Sustainable Innovation Practices:
    There is a growing emphasis on sustainability-driven innovation, particularly how organizations can align their strategies with environmental goals and circular economy principles.

Declining or Waning

While 'Industrial Management & Data Systems' continues to explore a broad array of topics, certain themes have shown a decline in prominence over recent years. This may reflect shifting research priorities or saturation in specific areas.
  1. Traditional Management Theories:
    There has been a noticeable decrease in papers focusing on classical management theories, as the field shifts towards more contemporary and technology-driven approaches.
  2. Purely Theoretical Frameworks:
    Research that does not incorporate empirical data or practical applications is becoming less common, with a greater emphasis on data-driven insights and case studies.
  3. Localized Case Studies:
    Studies focusing solely on localized contexts without broader implications or applications are waning, as there is a growing demand for research that offers global insights or cross-contextual analysis.

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