Predicting e-readiness at firm-level: An analysis of technological, organizational and environmental (TOE) effects on e-maintenance readiness in manufacturing firms

Aboelmaged, Mohamed Gamal

doi:10.1016/j.ijinfomgt.2014.05.002

Cited by 245 publications

(192 citation statements)

References 106 publications

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“…; is it always on or occasional usage?). This can be captured through detailed use case analysis with operational staff (Kefalidou et al 2015) IT knowledge-Following Aboelmaged (2014), it is critical to develop the IT competence that is required to develop and maintain a working predictive maintenance solution. Many of these competencies may be outside of the usual realms of competence for a sector (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…This might be the servers that can store large volumes of data, or telecommunications networks to allow the transfer of information from the asset to the control centre. Having a body of existing IT competence is deemed a critical factor in successful e-maintenance deployment (Aboelmaged 2014). This requires appropriate competency for the staff working the system both as technical skills and IT management skills (Chowdhury and Akram 2013).…”

Section: Within An Organisationmentioning

confidence: 99%

“…In particular, Aboelmaged (2014) demonstrated the relevance of the Technology, Organizational and Environment (TOE) framework, where environment includes not only the government, but suppliers, customers and competitors. Similarly, Jonsson et al (2010) also stress the criticality of boundary spanning information flows, including those beyond any given organisation.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

“…As described above, the small body of human factors work in remote condition monitoring has typically focussed on technology development (Ciocoiu et al 2015;Houghton and Patel 2015;Dadashi et al 2016) in specific settings and does not explicitly seek out cross-organisational issues, though there is evidence from both more organisational work (Jonsson et al 2010;Aboelmaged 2014) and human factors models (e.g. Wilson and Sharples 2015) that cross-organisational issues are relevant.…”

Section: Research Approachmentioning

confidence: 99%

“…In practice, there can be substantial barriers to adoption due to human issues such as adequate knowledge of how to interpret data, interpreting large data sets and adapting data and decision-making for different roles (Dadashi et al 2014). Equally, there may be organisational challenges in accommodating change to make sure operational strategy makes best of use of advanced data-driven analyses tools (Aboelmaged 2014;Ciocoiu et al 2015). These issues can be most challenging when a new ecosystem of sensors is being embedded within an existing network of assets that follow legacy organisational processes (Kefalidou et al 2015).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A cross-sector analysis of human and organisational factors in the deployment of data-driven predictive maintenance

Golightly

Kefalidou

Sharples

2017

Inf Syst E-Bus Manage

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Domains such as utilities, power generation, manufacturing and transport are increasingly turning to data-driven tools for management and maintenance of key assets. Whole ecosystems of sensors and analytical tools can provide complex, predictive views of network asset performance. Much research in this area has looked at the technology to provide both sensing and analysis tools. The reality in the field, however, is that the deployment of these technologies can be problematic due to user issues, such as interpretation of data or embedding within processes, and organisational issues, such as business change to gain value from asset analysis. 13 experts from the field of remote condition monitoring, asset management and predictive analytics across multiple sectors were interviewed to ascertain their experience of supplying data-driven applications. The results of these interviews are summarised as a framework based on a predictive maintenance project lifecycle covering project motivations and conception, design and development, and operation. These results identified critical themes for success around having a target-or decision-led, rather than data-led, approach to design; long-term resourcing of the deployment; the complexity of supply chains to provide data-driven solutions and the need to maintain knowledge across the supply chain; the importance of fostering technical competency in end-user organisations; and the importance of a maintenance-driven strategy in the deployment of data-driven asset management. Emerging from these themes are recommendations related to culture, delivery process, resourcing, supply chain collaboration and industry-wide cooperation.

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Section: Discussionmentioning

confidence: 99%

Section: Within An Organisationmentioning

confidence: 99%

Section: Theoretical Frameworkmentioning

confidence: 99%

Section: Research Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A cross-sector analysis of human and organisational factors in the deployment of data-driven predictive maintenance

Golightly

Kefalidou

Sharples

2017

Inf Syst E-Bus Manage

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Smart, sustainable, and resilient food supply chains in disruptive events context

Sezer,

Kazancoglu,

Mangla

et al. 2024

Bus Strat Env

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Food systems include dynamic and complex networks made up of all the actors, processes, and infrastructures, as well as their interactions. Because of the growing supply chain's (SC) vulnerability, fragility, and operational disturbances, managing disruptions has become a crucial problem in the food SC. Thus, companies aim to improve their resilience to deal with unexpected conditions. Thus, this study aims to analyze and frame the food SC's resilience, sustainability, and smartness considering different causal conditions (e.g., digital technology adaption, providing traceability, and enhancing collaboration). This study also analyzes the numerous and complex linkages between outcome and causal variables. Thus, this study includes data on the food industry operating in Turkey. The Fuzzy Set Qualitative Comparative Analysis has been used to analyze different companies' data. SC resilience performance conditions have been determined and analyzed. According to the result of this study, it can be said that enhanced flexibility structure and adaptability skills in the SC, increased IT capability, and integration of digital technologies into the SC process are the key components for achieving high SC resilience, sustainability, and smartness in the food SCs. This study suggests various implications for policymakers and managers in the food SCs.

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Artificial intelligence adoption among human resource professionals: Does market turbulence play a role?

Islam,

Aldaihani,

Saatchi

2023

Glob Bus Org Exc

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Drawing from the Technology Acceptance Model (TAM) and Technology‐Organization‐Environment (TOE) framework, this study investigates the factors that influence the adoption of artificial intelligence (AI) among human resource (HR) professionals in the tourism organizations of Malaysia. Responses were collected from 389 HR employees working at tourism organizations. Based on a partial least squares‐structural equation modeling (PLS‐SEM) analysis, results revealed that perceived ease of use, perceived usefulness, technology competency, top management support, and competitive pressure have significantly positive impacts on AI adoption in the tourism industry in Malaysia. The study's contribution to the TAM and TOE models is the inclusion of market turbulence as a moderating factor, which has been found to be a significant moderator as it enhances the understanding of HR practitioners and others regarding the factors that are most influential in AI adoption among HR employees in the tourism industry. The model developed in this study highlights how market turbulence can foster the relationship between AI adoption and organizations. In addition to the managerial and theoretical implications, the study also discusses its limitations and outlines directions for future research.

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Predicting e-readiness at firm-level: An analysis of technological, organizational and environmental (TOE) effects on e-maintenance readiness in manufacturing firms

Cited by 245 publications

References 106 publications

A cross-sector analysis of human and organisational factors in the deployment of data-driven predictive maintenance

A cross-sector analysis of human and organisational factors in the deployment of data-driven predictive maintenance

Smart, sustainable, and resilient food supply chains in disruptive events context

Artificial intelligence adoption among human resource professionals: Does market turbulence play a role?

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