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Article
Publication date: 31 July 2009

Zahir Irani

360

Abstract

Details

Transforming Government: People, Process and Policy, vol. 3 no. 3
Type: Research Article
ISSN: 1750-6166

Available. Open Access. Open Access
Article
Publication date: 7 July 2023

David Holger Schmidt, Dirk van Dierendonck and Ulrike Weber

This study focuses on leadership in organizations where big data analytics (BDA) is an essential component of corporate strategy. While leadership researchers have conducted…

13065

Abstract

Purpose

This study focuses on leadership in organizations where big data analytics (BDA) is an essential component of corporate strategy. While leadership researchers have conducted promising studies in the field of digital transformation, the impact of BDA on leadership is still unexplored.

Design/methodology/approach

This study is based on semi-structured interviews with 33 organizational leaders and subject-matter experts from various industries. Using a grounded theory approach, a framework is provided for the emergent field of BDA in leadership research.

Findings

The authors present a conceptual model comprising foundational competencies and higher order roles that are data analytical skills, data self-efficacy, problem spotter, influencer, knowledge facilitator, visionary and team leader.

Research limitations/implications

This study focuses on BDA competency research emerging as an intersection between leadership research and information systems research. The authors encourage a longitudinal study to validate the findings.

Practical implications

The authors provide a competency framework for organizational leaders. It serves as a guideline for leaders to best support the BDA initiatives of the organization. The competency framework can support recruiting, selection and leader promotion.

Originality/value

This study provides a novel BDA leadership competency framework with a unique combination of competencies and higher order roles.

Details

Journal of Management Development, vol. 42 no. 4
Type: Research Article
ISSN: 0262-1711

Keywords

Available. Content available
Book part
Publication date: 7 June 2024

Gennaro Maione

Free Access. Free Access

Abstract

Details

Sustainable Innovation Reporting and Emerging Technologies
Type: Book
ISBN: 978-1-83797-740-6

Available. Content available
Book part
Publication date: 21 January 2022

Abstract

Details

Industry 4.0 and Global Businesses
Type: Book
ISBN: 978-1-80117-326-1

Available. Open Access. Open Access
Article
Publication date: 4 March 2022

Mohammad Bahrami, Sajjad Shokouhyar and Atiyeh Seifian

Big data analytics (BDA) capabilities can affect supply chain performance in several ways. The main purpose of this study was to understand how BDA capabilities could affect…

10504

Abstract

Purpose

Big data analytics (BDA) capabilities can affect supply chain performance in several ways. The main purpose of this study was to understand how BDA capabilities could affect supply chain performance through supply chain resilience and supply chain innovation.

Design/methodology/approach

The study adopted a cross-sectional approach to collect survey-based responses to examine the hypotheses. Accordingly, 187 responses were collected and analyzed using partial least squares (PLS) in the SmartPLS3.

Findings

The results showed that BDA capabilities improve supply chain performance through resilience and innovation of the supply chain.

Originality/value

The present study also contributed to the existing literature by demonstrating the mediating role of supply chain resilience and supply chain innovation between BDA capabilities and supply chain performance. In this context, some theoretical and managerial implications were proposed and discussed.

Details

Modern Supply Chain Research and Applications, vol. 4 no. 1
Type: Research Article
ISSN: 2631-3871

Keywords

Available. Open Access. Open Access
Article
Publication date: 5 August 2021

Denis Dennehy, John Oredo, Konstantina Spanaki, Stella Despoudi and Mike Fitzgibbon

The purpose of this paper is to understand the nomological network of associations between collective mindfulness and big data analytics in fostering resilient humanitarian relief…

6637

Abstract

Purpose

The purpose of this paper is to understand the nomological network of associations between collective mindfulness and big data analytics in fostering resilient humanitarian relief supply chains.

Design/methodology/approach

The authors conceptualize a research model grounded in literature and test the hypotheses using survey data collected from informants at humanitarian aid organizations in Africa and Europe.

Findings

The findings demonstrate that organizational mindfulness is key to enabling resilient humanitarian relief supply chains, as opposed to just big data analytics.

Originality/value

This is the first study to examine organizational mindfulness and big data analytics in the context of humanitarian relief supply chains.

Details

International Journal of Operations & Production Management, vol. 41 no. 9
Type: Research Article
ISSN: 0144-3577

Keywords

Available. Open Access. Open Access
Article
Publication date: 13 March 2024

Abdolrasoul Habibipour

This study aims to investigate how living lab (LL) activities align with responsible research and innovation (RRI) principles, particularly in artificial intelligence (AI)-driven…

743

Abstract

Purpose

This study aims to investigate how living lab (LL) activities align with responsible research and innovation (RRI) principles, particularly in artificial intelligence (AI)-driven digital transformation (DT) processes. The study seeks to define a framework termed “responsible living lab” (RLL), emphasizing transparency, stakeholder engagement, ethics and sustainability. This emerging issue paper also proposes several directions for future researchers in the field.

Design/methodology/approach

The research methodology involved a literature review complemented by insights from a workshop on defining RLLs. The literature review followed a concept-centric approach, searching key journals and conferences, yielding 32 relevant articles. Backward and forward citation analysis added 19 more articles. The workshop, conducted in the context of UrbanTestbeds.JR and SynAir-G projects, used a reverse brainstorming approach to explore potential ethical and responsible issues in LL activities. In total, 13 experts engaged in collaborative discussions, highlighting insights into AI’s role in promoting RRI within LL activities. The workshop facilitated knowledge sharing and a deeper understanding of RLL, particularly in the context of DT and AI.

Findings

This emerging issue paper highlights ethical considerations in LL activities, emphasizing user voluntariness, user interests and unintended participation. AI in DT introduces challenges like bias, transparency and digital divide, necessitating responsible practices. Workshop insights underscore challenges: AI bias, data privacy and transparency; opportunities: inclusive decision-making and efficient innovation. The synthesis defines RLLs as frameworks ensuring transparency, stakeholder engagement, ethical considerations and sustainability in AI-driven DT within LLs. RLLs aim to align DT with ethical values, fostering inclusivity, responsible resource use and human rights protection.

Originality/value

The proposed definition of RLL introduces a framework prioritizing transparency, stakeholder engagement, ethics and sustainability in LL activities, particularly those involving AI for DT. This definition aligns LL practices with RRI, addressing ethical implications of AI. The value of RLL lies in promoting inclusive and sustainable innovation, prioritizing stakeholder needs, fostering collaboration and ensuring environmental and social responsibility throughout LL activities. This concept serves as a foundational step toward a more responsible and sustainable LL approach in the era of AI-driven technologies.

Details

Journal of Information, Communication and Ethics in Society, vol. 22 no. 2
Type: Research Article
ISSN: 1477-996X

Keywords

Available. Open Access. Open Access
Article
Publication date: 23 September 2024

Fredrick Ishengoma and Elia John

This study aims to establish a comprehensive framework for adopting mobile-based artificial intelligence (AI) services in Tanzanian manufacturing small and medium enterprises…

698

Abstract

Purpose

This study aims to establish a comprehensive framework for adopting mobile-based artificial intelligence (AI) services in Tanzanian manufacturing small and medium enterprises (SMEs).

Design/methodology/approach

The methodology involved conducting a literature review and using the combination of Mobile Services Acceptance Model and Innovation Diffusion Theory (IDT) as a theoretical foundation. This synthesis delves into the current knowledge on technology adoption, organizational behavior and innovation diffusion, creating a solid conceptual basis. Expert review was used for framework validation to ensure the framework's accuracy.

Findings

This study shows that the factors influencing the adoption of mobile-based AI services in Tanzanian manufacturing SMEs include perceived usefulness, perceived ease of use, context, personal initiatives and characteristics, trust, infrastructure, cost, mobility, power distance, compatibility, observability and trialability.

Research limitations/implications

The framework provides valuable insights tailored to Tanzanian sociocultural and economic nuances. However, its generalizability is limited due to its specificity to Tanzanian manufacturing SMEs.

Practical implications

The framework outlined in this research provides SME leaders, policymakers and technology implementers with valuable guidance to make informed decisions during the adoption process.

Originality/value

This study introduces a novel lens for understanding technology adoption. This study's focus on the Tanzanian context and its nuanced examination of contributing factors add to its originality and practical significance.

Details

Vilakshan - XIMB Journal of Management, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0973-1954

Keywords

Available. Open Access. Open Access
Article
Publication date: 18 July 2023

Tomasz Mucha, Sijia Ma and Kaveh Abhari

Recent advancements in Artificial Intelligence (AI) and, at its core, Machine Learning (ML) offer opportunities for organizations to develop new or enhance existing capabilities…

1617

Abstract

Purpose

Recent advancements in Artificial Intelligence (AI) and, at its core, Machine Learning (ML) offer opportunities for organizations to develop new or enhance existing capabilities. Despite the endless possibilities, organizations face operational challenges in harvesting the value of ML-based capabilities (MLbC), and current research has yet to explicate these challenges and theorize their remedies. To bridge the gap, this study explored the current practices to propose a systematic way of orchestrating MLbC development, which is an extension of ongoing digitalization of organizations.

Design/methodology/approach

Data were collected from Finland's Artificial Intelligence Accelerator (FAIA) and complemented by follow-up interviews with experts outside FAIA in Europe, China and the United States over four years. Data were analyzed through open coding, thematic analysis and cross-comparison to develop a comprehensive understanding of the MLbC development process.

Findings

The analysis identified the main components of MLbC development, its three phases (development, release and operation) and two major MLbC development challenges: Temporal Complexity and Context Sensitivity. The study then introduced Fostering Temporal Congruence and Cultivating Organizational Meta-learning as strategic practices addressing these challenges.

Originality/value

This study offers a better theoretical explanation for the MLbC development process beyond MLOps (Machine Learning Operations) and its hindrances. It also proposes a practical way to align ML-based applications with business needs while accounting for their structural limitations. Beyond the MLbC context, this study offers a strategic framework that can be adapted for different cases of digital transformation that include automation and augmentation of work.

Available. Open Access. Open Access
Article
Publication date: 8 July 2024

Tim Kastrup, Michael Grant and Fredrik Nilsson

New digital technologies are reshaping the business landscape and accounting work. This paper aims to investigate how incorporating more data and new data analytics (DA) tools…

730

Abstract

Purpose

New digital technologies are reshaping the business landscape and accounting work. This paper aims to investigate how incorporating more data and new data analytics (DA) tools impacts the role and use of judgment in financial due diligence (FDD).

Design/methodology/approach

The paper reports findings from a field study at a Big Four accounting firm in Sweden (“DealCo”). The primary data includes semi-structured interviews, observations and other meetings. Theoretically, it draws on Dewey’s The Logic of Judgments of Practise and Logic: The Theory of Inquiry and distinguishes between theoretical (what is probably true) and practical judgment (what to do).

Findings

In DealCo’s FDD practice, using more data and new DA tools meant that the realm of possibility had expanded significantly. To manage the newfound abundance and to use DA effectively, DealCo’s advisors invoked practical and theoretical judgments in different stages and areas of the data-driven FDD. The paper identifies four critical uses of judgment: Setting priorities and exercising restraint (practical judgment) and forming hypotheses and doing sense checks (theoretical judgment). In these capacities, practical judgment and theoretical judgment were essential in transforming raw data into actionable insights and, in effect, an indeterminate situation into a determinate one.

Originality/value

The study foregrounds the practical dimension of knowledge production for decision-making and contributes to a better understanding of the role, use and importance of accounting professionals’ judgment in a data-driven world.

Details

Accounting, Auditing & Accountability Journal, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0951-3574

Keywords

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