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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 critical importance of digital transformation in enhancing industrial growth and competitiveness, especially in heavy construction, this study introduces a tailored capability assessment model and self-appraisal tool for firms in this sector. These resources enable them to gauge their readiness for adopting digital technology effectively.

Utilizing the Technology—Organization—Environment (TOE) and Natural Resource Dependence Theory (NRDT) frameworks, 22 markers were identified to structure a questionnaire distributed to construction professionals. Descriptive analysis and fuzzy synthetic evaluation (FSE) were used to develop the capability assessment model. A validation survey assessed the validity of both the model and the self-appraisal instrument.

The study identified the top five significant markers: (1) leadership commitment to digital transformation, (2) workforce readiness for technology integration, (3) potential ROI through efficiency gains, (4) technology maturity for construction applications and (5) complexity of integrating new technologies with existing workflows. Through FSE, the most critical factors were technology-related, organizational and resource optimization markers.

By employing the TOE and NRDT frameworks, the study identifies the most critical factors influencing digital adoption in heavy construction. Also, the user-friendly self-appraisal instrument developed in this study can be considered a valuable contribution, as it provides heavy construction firms with a practical tool for ongoing monitoring and improvement of their digital transformation efforts.

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.

In this paper, a graph theory-based maturity model to comprehensively assess the Industry 4.0 maturity level by means of a multidimensional Industry 4.0 maturity score (MIMS) is proposed.

The proposed maturity assessment model is based on (1) the identification of the maturity dimensions and assessment parameters from existing literature and expert opinion and (2) the development of the assessment model based on graph theory and matrix-based approach. The illustrative application of the developed model in two case enterprises is also included.

The model is configured to include six dimensions having 30 constituent attributes. Unlike the case with many of the previously published models, the proposed model does not evaluate the progress in specific Industry 4.0 enabling technologies, rather the assessment parameters are associated with the functional areas.

While the configuration of the proposed model enables a comprehensive maturity assessment, it facilitates the identification of contextual dimensional attributes and assists the enterprises in developing the roadmap for Industry 4.0 implementation aligning to the diverse organizational strategies.

Distinctively, the proposed model apprehends the interdependency between the maturity assessment dimensions and their constituent attributes. Also, the maturity model incorporates the assessment of the maturity in the dimension of inbound and outbound integration. While these two dimensions are crucial in the Industry 4.0 ecosystem, to the best of our knowledge, these are not considered by any of the assessment models published so far.

A maturity model for Industry 4.0 (I4.0 MM) with influencing factors is designed to address maturity issues in adopting Industry 4.0. Standardisation in I4.0 supports manufacturing industry transformation, forming reference architecture models (RAMs). This paper aligns key factors and maturity levels in I4.0 MMs with reputable I4.0 RAMs to enhance strategy for I4.0 transformation and implementation.

Three steps of alignment consist of the systematic literature review (SLR) method to study the current published high-quality I4.0 MMs, the taxonomy development of I4.0 influencing factors by adapting and implementing the categorisation of system theories and aligning I4.0 MMs with RAMs.

The study discovered that different I4.0 MMs lead to varied organisational interpretations. Challenges and insights arise when aligning I4.0 MMs with RAMs. Aligning MM levels with RAM stages is a crucial milestone in the journey toward I4.0 transformation. Evidence indicates that I4.0 MMs and RAMs often overlook the cultural domain.

Findings contribute to the literature on aligning capabilities with implementation strategies while employing I4.0 MMs and RAMs. We use five RAMs (RAMI4.0, NIST-SME, IMSA, IVRA and IIRA), and as a common limitation in SLR, there could be a subjective bias in reading and selecting literature.

To fully leverage the capabilities of RAMs as part of the I4.0 implementation strategy, companies should initiate the process by undertaking a thorough needs assessment using I4.0 MMs.

The novelty of this paper lies in being the first to examine the alignment of I4.0 MMs with established RAMs. It offers valuable insights for improving I4.0 implementation strategies, especially for companies using both MMs and RAMs in their transformation efforts.

This study investigated university lecturers' attitudes, readiness and anxiety toward e-learning in response to the widespread transition to online education prompted by the COVID-19 pandemic and the interrelationships between these variables using a sample drawn from Nigeria.

The sample consisted of 168 university lecturers in Nigeria, comprising 94 males and 74 females. A questionnaire assessing university lecturers' attitudes, readiness and anxiety toward e-learning was administered to collect data. Descriptive, t-test and regression analyses were conducted to analyze the collected data.

The findings revealed significant correlations among lecturers' e-learning attitudes, readiness and anxiety. Specifically, significant relationships were observed between lecturers' e-learning attitudes and readiness, lecturer’s e-learning readiness and anxiety, as well as lecturers' e-learning anxiety and e-learning attitudes. Moreover, a notable difference was detected in the mean scores of e-learning attitudes between male and female lecturers, indicating that male lecturers exhibited more positive e-learning attitudes compared to their female counterparts. Additionally, male lecturers reported lower levels of e-learning anxiety and demonstrated higher readiness to embrace e-learning compared to female lecturers.

The COVID-19 pandemic posed significant limitations for this study. Firstly, due to restrictions on in-person interactions and mobility, conducting a pilot study was not feasible, which may have impacted the refinement of our methodologies. Additionally, the number of participants was limited as a result of challenges in recruitment and accessibility during the pandemic, potentially affecting the generalizability of our findings. Future research efforts should aim to mitigate these constraints and consider alternative methods for data collection to enhance the robustness of the results. Another research implication is the need to explore, through a qualitative approach, the lecturer’s use of e-learning and perception of the process, outcome and needs or areas of improvement. This could unearth deeper issues that threaten the effective transition to and use of e-learning by higher education teachers.

Overall, our findings illuminate the importance of targeted policy and practice interventions to address attitudes, readiness and anxiety among lecturers, facilitating the successful implementation of e-learning initiatives within Nigerian higher education institutions.

The study underscores implications for e-learning integration, emphasizing the importance of considering various human and social factors alongside technical aspects.

This study adds to the existing literature by examining the level of lecturers' attitudes, readiness and anxiety toward the adoption of mandatory e-learning in schools and how these variables relate to drawings on a Nigerian sample.

This paper discusses plausible future scenarios for small and medium tourism enterprises (SMTEs) in the “sun, sea and sand” destination of the Canary Islands (Spain) and assesses to what extent they are prepared to adjust to market changes and technological developments in the light of both sudden disruptions and long-term shocks.

A scenario analysis was made based upon expert interviews, leading to a 2 × 2 scenario matrix.

Although regional, national and European strategies advocate digital transformation as a step towards building resilience and towards a more sustainable future, this study identifies two major uncertainties that can put that transformation at risk: a change of the traditional “sun, sand and sea” visitor to a more conscious, individual and inquisitive traveller or “Promad”, and the business culture of SMTEs.

Resilience for sudden and for slow-paced disruptions poses different challenges for SMTEs. Their next step in the digital transformation must take them form marketing and sales-oriented e-business to growing interconnectivity and innovation across supply chains.

A market change towards the “Promad” type of traveller causes at least a temporary mismatch of demand and supply. As many SMTEs miss either the knowledge or the resources to invest in digital transformation, the process will depend on support and coordination at destination level.

The study identifies, with the example of the Canary Islands, the difficulties for destinations and individual businesses in making the envisioned transition of mass tourism to more competitive forms of tourism with a smaller ecological footprint.

This article aims to examine whether the organizational changes brought about by digital transformation (DT) -such as a new organizational culture, new leadership and new business models-influence talent management, with the latter being seen as one of the major challenges facing companies in their process of digital transformation.

Using a quantitative methodology, a survey was applied to 314 companies in order to analyze the results of their talent management in the DT process. DT is not only digitalization as demonstrated in this study. Talent management is the key piece that can facilitate or block achieving high levels of digital maturity.

The study finds that the changes brought about by DT impact talent attraction, talent retention, and talent management in general, and also shows that digital transformation does not depend on digitalization, but rather that talent management is the key to either helping or preventing high levels of digital maturity being achieved.

The originality of this work lies in examining the influence of the changes that DT entails in talent management.

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.

Considering the swift adoption of innovative sustainability practices in businesses to accomplish sustainable development goals (SDGs), research on corporate sustainability has increased significantly over the years. This research intends to analyze the published literature, emphasizing the existing, emerging and future research directions on achieving the SDGs through corporate sustainability.

This research analyzed the growing trends in corporate sustainability by incorporating 2,038 Scopus articles published between 1999 and 2022 using latent Dirichlet allocation (LDA) topic modeling, bibliometrics and qualitative content analysis techniques. The bibliometric data were analyzed using performance and science mapping. Thereafter, topic modeling and content analysis uncovered the topics included under the corporate sustainability umbrella.

The findings indicate that investigation into corporate sustainability has considerably increased from 2015 to date. Additionally, the majority of studies on corporate sustainability are from the United States of America, the United Kingdom and Germany. Besides, the USA has the most collaboration in terms of co-authorship. S. Schaltegger was considered the most productive author. However, P. Bansal was ranked as the top author based on a co-citation analysis of authors. Further, bibliometric data were evaluated to analyze leading publications, journals and institutions. Besides, keyword co-occurrence analysis, topic modeling and content analysis highlighted the theoretical underpinnings and new patterns and provided directions for further research.

This study demonstrates various existing and emerging themes in corporate sustainability, which have various repercussions for academicians and organizations. This research also examines the lagging themes in the current domain.