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This study aims to establish an assessment model to measure big data readiness in the public sector, specifically targeting local governments at the provincial and city/regency levels. Additionally, the study aims to gain valuable insights into the readiness of selected local governments in Indonesia by using the established assessment model.

This study uses a mixed-method approach, using focus group discussions (FGDs), surveys and exploratory factor analysis (EFA) to establish the assessment model. The FGDs involve gathering perspectives on readiness variables from experts in academia, government and practice, whereas the survey collects data from a sample of selected local governments using a questionnaire developed based on the variables obtained in FGDs. The EFA is used on survey data to condense the variables into a smaller set of dimensions or factors. Ultimately, the assessment model is applied to evaluate the level of big data readiness among the selected Indonesian local governments.

FGDs identify 32 essential variables for evaluating the readiness of local governments to adopt big data. Subsequently, EFA reduces this number by five and organizes the remaining variables into four factors: big data strategy, policy and collaboration, infrastructure and human resources and data collection and utilization. The application of the assessment model reveals that the overall readiness for big data in the selected local governments is primarily moderate, with those in the Java cluster displaying higher readiness. In addition, the data collection and utilization factor achieves the highest score among the four factors.

This study offers an assessment model for evaluating big data readiness within local governments by combining perspectives from big data experts in academia, government and practice.

Due to the growing complexity involved in leveraging the endless possibilities of ICT on all levels, the technical competence of faculties of higher education institutions (HEI) and effective methods for fostering e-readiness has become questionable.

This research has developed and validated an empirically supported e-readiness scale, which can be used by HEIs to assess faculty members’ preparedness toward online teaching. The measurement model and the structural model were developed as the results of exploratory factor analysis and confirmatory factor analysis (n = 245). The previously identified components and their indicators were validated using the structural models and the final scale was developed with five dimensions (“online technological readiness, pedagogical readiness, institutional readiness, learning and delivery readiness and content readiness”).

The faculties’ e-readiness assessment tool, as a useful tool, could aid institutions in identifying problems that affect the implementation of e-learning or digitalization in the institutions and developing strategies in response.

Like any research this research also has some limitations and can be considered as future research probability like the responses for this research were collected from HEI in India; however, a cross-cultural study can be conducted to understand the parameters across the globe. Although the psychometric qualities of the e-readiness scale are acceptable, additional research in various higher educational environments, both nationally and internationally, is required to further establish the scale’s relevance, validation and generalizability.

Although many scales have been developed to assess the readiness level in the education sector, a scale, that holistically measures, the readiness level of faculties from an overall perspective was required. This scale can be used to recognize the e-readiness level of teachers in HEIs. This scale can also help the institutions assess the readiness level of their faculty members and address any improvements required in their teaching and learning pedagogy, further acknowledging training needs.

Considering the importance and the broad applications of the Fourth Industrial Revolution in various organizations and industries and enjoying the many benefits of this digital transformation framework, organizations need to measure their Industry 4.0 readiness as a starting point and take steps to achieve the strategic goals of Industry 4.0. This study aims to design a comprehensive and practical model that can determine Industry 4.0 readiness level, allowing organizations to implement and exploit technological constituents of this phenomenon.

A systematic literature review (SLR) methodology was used to evaluate and summarize a clear and comprehensive literature overview of Industry 4.0 readiness models and to certify the validity and transparency of the review process. After reviewing 71 articles and survey and then the consensus of Industry 4.0 experts, the 10 dimensions of the 4.0 Industry readiness model were finalized with their indicators having the most frequency in the published articles and models.

The application of the SLR to the development of the new Industry 4.0 readiness model which includes 10 dimensions and 37 indicators and can assess the Industry 4.0 readiness of firms and industries accurately and effectively.

An extensive review of the previous literature yielded the current Industry 4.0 readiness model. The comprehensiveness of this model leads to its wide application in different companies. Future research suggestions are presented at the end of the manuscript.

The concept of the Fourth Industrial Revolution and the application of its technologies are vague and complicated for many organizations and managers, while the need to implement the components and technologies of Industry 4.0 is essential to achieve organizational goals. The presented readiness model helps companies to measure their readiness to enter the Fourth Industrial Revolution and achieve long-term goals.

In this study, an attempt was made to examine the Industry 4.0 readiness models thoroughly and extensively and identify their different approaches. Finally, a comprehensive and multi-dimensional readiness model is presented to assess the position of organizations in order to enter Industry 4.0.

Industry 4.0 has offered significant potential for manufacturing firms to alter and rethink their business models, production processes, strategies and objectives. Manufacturing organizations have recently undergone substantial transformation due to Industry 4.0 technologies. Hence, to successfully deploy and embed Industry 4.0 technologies in their organizational operations and practices, businesses must assess their adoption readiness. For this purpose, a multi-dimensional analytical indicator methodology has been developed to measure Industry 4.0 maturity and preparedness.

A weighted average method was adopted to assess the Industry 4.0 readiness using a case study from a steel manufacturing organization.

The result revealed that the firm ranks between Industry 2.0 and Industry 3.0, with an overall score of 2.32. This means that the organization is yet to achieve Industry 4.0 mature and ready organization.

The multi-dimensional indicator framework proposed can be used by managers, policymakers, practitioners and researchers to assess the current status of organizations in terms of Industry 4.0 maturity and readiness as well as undertake a practical diagnosis and prognosis of systems and processes for its future adoption.

Although research on Industry 4.0 maturity models has grown exponentially in recent years, this study is the first to develop a multi-dimensional analytical indicator to measure Industry 4.0 maturity and readiness.

The main purpose of this research is to (1) prioritise key determinants of Industry 4.0 (I4.0) readiness assessment and (2) evaluate causal relationships among those determinants and associated sub-criteria based on inputs from industry experts.

The methodology involved two phases: (1) an MCDM approach for determining causal relationships among determinants and (2) empirical validation of findings from the first phase using industry experts' inputs.

It was found that while the choice of I4.0 technologies is important, organisational factors are also critical, as evidenced by the ranking of the “Strategy and Organisation” determinant as the highest rank prominent determinant. Also, the ranking of the sub-criteria within each determinant shows the importance of several organisational influencing and resulting sub-criteria.

This research extends the existing literature on I4.0 by demonstrating the prioritisation of determinants and delineating causal relationships among them and associated sub-criteria as a basis for developing I4.0 adoption guidelines. This research is limited to the specific scope of determinants selected/considered and experts' inputs from the Sri Lankan manufacturing sector. Future studies could consider extending this research into a broader global manufacturing context.

Prioritisation and causal relationships of I4.0 readiness assessment determinants, supported with inputs from functional managers and industry experts, could be used to guide practitioners in developing guidelines for I4.0 adoption in a phased manner.

This research provides a re-evaluation and validation of a selected I4.0 readiness assessment framework from the perspectives of interdependencies and casual relationships among its determinants and sub-criteria, based on inputs from industry experts as a basis for developing guidelines for I4.0 adoption.

The fourth industrial revolution and digital transformation have caused paradigm changes in the procedures of goods production and services through disruptive technologies, and they have formed new methods for business models. Health and medicine fields have been under the effect of these technology advancements. The concept of smart hospital is formed according to these technological transformations. The aim of this research, other than explanation of smart hospital components, is to present a model for evaluating a hospital readiness for becoming a smart hospital.

This research is an applied one, and has been carried out in three phases and according to design science research. Based on the previous studies, in the first phase, the components and technologies effecting a smart hospital are recognized. In the second phase, the extracted components are prioritized using type-2 fuzzy analytic hierarchical process based on the opinion of experts; later, the readiness model is designed. In the third phase, the presented model would be tested in a hospital.

The research results showed that the technologies of internet of things, robotics, artificial intelligence, radio-frequency identification as well as augmented and virtual reality had the most prominence in a smart hospital.

The innovation and originality of the forthcoming research is to explain the concept of smart hospital, to rank its components and to provide a model for evaluating the readiness of smart hospital. Contribution of this research in terms of theory explains the concept of smart hospital and in terms of application presents a model for assessing the readiness of smart hospitals.

Construction industry (CI) has great prominence for the world economy, and it is expected that, with the use of the innovative technologies and approaches of Industry 4.0 (I4.0), the new industrial paradigm, construction can reach higher levels of productivity. This study aims to develop a model (readiness model) to assess the level of use of I4.0 technologies by the construction sector in Brazil and its most relevant applications.

The methodology used was bibliographic research, design-science research and a survey to validate the model, carried out with 162 companies, considered among the main ones in the sector in Brazil. The literature review revealed 13 technologies of I4.0 applied to construction; hence, the views of industry experts were based on these technologies.

The Digital Advancement Within CoNstruction (DAWN) readiness model was proposed, showing that among the 13 evaluated technologies of I4.0 and their applications, the Brazilian construction companies had a low level of utilization; both high and middle-income companies presented this low level of use; some technologies with a greater number of scientific publications were less used in practice in the Brazilian construction.

The originality and theoretical contribution are to present a readiness model to assess the level of use of I4.0 technologies and their most relevant applications in the CI in countries with an economy similar to Brazil’s, making it possible to measure the level of adoption of these technologies.

This study aims to develop a maturity model designed for assessing the current state of digitization in accounting firms.

The authors have developed this index where the maturity levels are defined from the life cycle theory. For the items of a maturity measure, the authors have adopted a multimethodological approach. That approach allows to identify 27 measurement items to cover the three dimensions of audit, reporting and taxation.

This research proposes a diagnostic tool specific to accounting firms. The authors have tested this index in the Tunisian context. The results show that there are two types of accounting firms. This study found the first firm in the embryonic phase and the other in the growth phase. This points out the active role of Tunisian accounting firms in technology integration.

This study highlights the integration of technology in the accounting field. Specifically, it aims to address technology management in accounting firms by measuring the degree of digitization of accounting firms. This research projects the use of information technologies (artificial intelligence, cloud, big data, etc.) in auditing, reporting and taxation.

On a practical level, this research provides an organizational diagnostic tool to assess the status of their accounting firms in terms of digitization. This will motivate practitioners to make frequent assessments, thus contributing to continuous improvement toward digitization.

The theoretical foundation of this research is based on the theory of the life cycle of technologies. This study is using this theory to identify and describe the current phase of the organization. And that is by indicating the overall scores on the technological capabilities of the accounting firms.

The study aims to review the currently available Industry 4.0 (I4.0) maturity models (MMs) for manufacturing industries and analyse their applicability in the oil and gas (O&G) upstream sector. Knowing that the growth in demand for energy through crude oil and natural gas is still viable over the next decade, there is the drive to ensure sustenance and improvement in production. The study sees an opportunity in harnessing the gains of Industry 4.0 technologies for better solution-driven strategies in production processes, equipment availability and reliability which would translate into higher production performance. So, a review on the Industry 4.0 MMs is considered important.

A systematic and in-depth literature review was performed to identify the specific requirements of this industry. This study examined the key characteristics of the O&G upstream sector and identified research gaps that need to be addressed to successfully support this industry for Industry 4.0 implementation. An Industry 4.0 MM that reflects the industrial realities for this industry more accurately from insights drawn from reviews of existing MMs is proposed

The review of 19 selected Industry 4.0 MMs revealed that the existing MMs are not a direct fit for the O&G upstream industry. Only a few of the models were clear on validation but with subjectivity, low number of persons and industries involved as limitations; none of the models confirmed validation with the O&G industry. There are varying views on the model dimensions and maturity levels by each author and not all required areas specific to the O&G industries were acknowledged by the models. An MM specific to this industry is therefore required.

Although the journey of digitisation has commenced in the O&G industry, a reduction with the challenges of transition towards Industry 4.0 implementation and provision of support for improved efficiency is assured using a robust MM, as proposed in this paper.

Smart manufacturing is revolutionizing the manufacturing industry by shifting the focus from traditional manufacturing to a more intelligent, interconnected and responsive system. Despite being the backbone of the economy and despite the government’s efforts in supporting and encouraging the transformation to smart manufacturing, small and medium enterprises (SMEs) have been struggling to transform their operations. This study aims to identify the challenges for SMEs’ transformation and the benefits they can get from this transformation, following a systematic review of existing literature.

A systematic review of existing literature has been performed to identify the peer-reviewed journal articles that focus on smart manufacturing for SMEs. First, a comprehensive list of keywords relevant to the review questions are identified. Second, Scopus and Web of Science databases were then used to search for articles, applying filters for English language and peer-reviewed status. Third, after manually assessing abstracts for relevance, 175 articles are considered for further review and analysis.

The benefits and challenges of SMEs’ transformation to smart manufacturing are identified. The identified challenges are categorized using the Smart Industry Readiness Index (SIRI) framework. Further, to address the identified challenges and initiate the SME’s transition toward smart manufacturing, a framework has been proposed that shows how SMEs can start their transition with minimum investment and existing resources.

Several studies have concentrated on understanding how smart manufacturing enhances sustainability, productivity and preventive maintenance. However, there is a lack of studies comprehensively analyzing the challenges for smart manufacturing adoption for SMEs. The originality of this study lies in identifying the challenges and benefits of smart manufacturing transformation and proposing a framework as a roadmap for SMEs' smart manufacturing adoption.