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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.

Production systems occupy geographically dispersed organizations with limited visibility and transparency. Such limitations create operational inefficiencies across the Supply Chain (SC). Recently, researchers have started exploring applications of Digital Twins Technology (DTT) to improve SC operations. In this context, there is a need to provide comprehensive theoretical knowledge and frameworks to help stakeholders understand the adoption of DTT. This study aims to fulfill the research gap by empirically investigating DTT readiness to enable transparency in SC.

A comprehensive literature survey was conducted to develop a theoretical model related to Supply Chain Transparency (SCT) and DTT readiness. Then, a questionnaire was developed based on the proposed theoretical model, and data was collected from Indian manufacturers. The data was analyzed using Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM) to confirm the proposed relationships.

The findings from the study confirmed a positive relationship between DTT implementation and SCT. This study reported that data readiness, perceived values and benefits of DTT, and organizational readiness and leadership support influence DTT readiness and further lead to SCT.

This study contributes to the literature and knowledge by uniquely mapping and validating various interactions between DTT readiness and sustainable SC performance.

The purpose of this paper is to investigate the organizational readiness perspective of adopting artificial intelligence and big data analytics in the textile and garment industry in Bangladesh along with identifying the associated factors.

The research uses a qualitative method using semi-structured interviews with representatives of business organizations and stakeholders of Bangladesh’s textile and garment industry.

The research reveals that the textile and garment industry in Bangladesh currently has low organizational readiness to adopt artificial intelligence and big data analytics. This is due to moderate knowledge- and leadership-readiness along with low human-, finance- and engagement-readiness of most of the business organizations. The readiness aspects interplay with each other and need to be improved holistically.

Considering the significant global and national importance of Bangladesh’s textile and garment industry, gaining insights into the industry’s current state of readiness for adopting artificial intelligence and big data analytics would offer valuable assistance to both national and global economies and may enhance economic outcomes.

Since no exploratory study was conducted to understand the organizational readiness aspects of adopting artificial intelligence and big data analytics of the globally significant textile and garment industry in Bangladesh, the paper analyzes five key aspects of such readiness and offers a basis for conducting similar studies in other emerging economies.

While the adoption of health-analytics (HA) is expanding, not every healthcare organization understands the factors impacting its readiness for HA. An assessment of HA-readiness helps guide organizational strategy and the realization of business value. Past research on HA has not included a comprehensive set of readiness-factors and assessment methods. This study’s objective is to design artifacts to assess the HA-readiness of hospitals.

The information-systems (IS) theory and methodology entail the iterative Elaborated Action Design Research (EADR)method, combined with cross-sectional field studies involving 14 healthcare organizations and 27 participants. The researchers determine factors and leverage multi-criteria decision-making techniques to assess HA-readiness.

The artifacts emerging from this research include: (1) a map of readiness factors, (2) multi-criteria decision-making techniques that assess the readiness levels on the factors, the varying levels of factor-importance and the inter-factor relationships and (3) an instantiated system. The in-situ evaluation shows how these artifacts can provide insights and strategic direction to an organization through collective knowledge from stakeholders.

This study finds new factors influencing HA-readiness, validates the well-known and details their industry-specific nuances. The methods used in this research yield a well-rounded HA readiness-assessment (HARA) approach and offer practical insights to hospitals.

The application of knowledge management (KM) in government agencies is one strategy to deal with government problems effectively and efficiently. This study aims to identify KM readiness critical success factors (CSFs), measure the level of readiness for KM implementation, identify improvement initiatives and develop KM readiness models for government agencies. This model plays a role in the implementation of KM successful.

The level of readiness is obtained by calculating the factor weights of the opinions of experts using the entropy method. The readiness value is calculated from the results of the questionnaire with average descriptive statistics. The method for analysis of improvement initiatives adopts the Asian Productivity Organization framework. The model was developed based on a systems approach and expert validation.

Reliability testing with a Cronbach’s alpha value for entropy is 0.861 and the questionnaire is 0.920. The result of measuring KM readiness in government agencies is 75.29% which is at level 3 (ready/needs improvement). The improvement in the level of readiness is divided into two parts: increasing the value of factors that are still less than ready (75%) and increasing the value of all factors to level 4 (84%). The model consists of three main sections: input (KMCSFs), process (KM readiness) and output (KM implementation).

The first suggestion is that the sample of employees used in this study is still in limited quantities, that is, 50% of the total population. The second limitation is determining KMCSFs. According to experts, combining this study with factor search and correlation computations would make it more complete. The expert’s advice aims to obtain factors that can be truly tested both subjectively and objectively. Finally, regarding literature selection for future research, it is recommended to use a systematic literature review such as the preferred reporting items for systematic reviews and meta-analyses and Kitchenham procedures.

The management must also prioritize KMCSF according to its level and make KMCSF a key performance indicator. For example, at the priority level, active leadership in KM is the leading performance indicator of a leader. Then at the second priority level, management can make a culture of sharing an indicator of employee performance through a gamification program. The last point that management must pay attention to in implementing all of these recommendations is to collaborate with relevant stakeholders, for example, those authorized to draft regulations and develop human resources.

This study proposes a novel comprehensive framework to measure and improve KM implementation readiness in government agencies. This study also proposes a KMCSF and novel KM readiness model with its improvement initiatives through this framework.

This research is motivated to explore communication professionals' understanding of the digital changes brought by the Industry 4.0 revolution and how such changes may affect the strategies and skills expected in effective communication management. A specific focus of the research is to define the concept of Readiness for Industry 4.0 in communication and propose a theoretical framework to address the key dimensions of Readiness for Industry 4.0 as related to communication management.

A mixed research design was employed to fulfill the goal of this research. First, the authors took a grounded theory approach in proposing, conceptualizing and constructing the concept of Readiness for Industry 4.0 by reviewing a wider literature on technology and communication. As part of the conceptualization process, the authors proposed five dimensions which encompass the complexity of building capacity in communication practice to effectively manage changes associated with Industry 4.0. Second, the authors used a qualitative research method, in-depth interviews, to gain insights from 16 senior communication professionals working in South Korea.

The study’s interview results confirmed the challenge in finding a universal definition of Readiness for Industry 4.0, even though the interviewed senior communication professionals have widely recognized the changes in the workplace brought by the Industry 4.0. Our interviewees agreed that their mindset is ready for the changes. However, they addressed the need for communication professionals to continue to learn and build their knowledge and skills from multiple perspectives. More specifically, skill sets and knowledge in cognitive analytics, data management, technology literacy, sense making skills for digital transformation and digital competencies in crisis management are desired and necessary.

This research advances theory building in communication management by addressing the importance of digital competencies in the workplace. By proposing a theoretical framework to explain the Readiness for Industry 4.0, this article contributes to our knowledge of digital transformation and its impact on effective communication. Moreover, by having deep conversations with industry leaders who are in the forefront of managing the challenges associated with technology advancement, this article enriches its practical implications by linking the discussion to the proposed theoretical framework.

The purpose of this paper is to investigate the data profile of manufacturing small and medium enterprises (SMEs) with specific emphasis on understanding the data readiness of SMEs for discrete event simulation (DES) modelling.

Research was conducted through a review of literature and a survey research strategy of manufacturing SMEs.

This paper illustrates the data profile of manufacturing SMEs. Insight is provided on the types of data collected by SMEs, the collection methods used and how these data are stored by the SMEs. Additionally size and age effects are considered. Based on this data profile, conclusions are made regarding an indication of data readiness of manufacturing SMEs for DES modelling.

This research is focused specifically on manufacturing SMEs in Ireland, other countries and sectors are not investigated.

This paper provides owner-managers and senior management insight into the data profile of manufacturing SMEs and their potential for utilisation of DES for performance improvement and decision support.

This paper addresses the gaps that exist in the knowledge of the data profile of manufacturing SMEs and consequently the status of this profile with regard to the readiness of SMEs for DES modelling.

This study aims to identify the readiness of institutional repositories in Indonesia to implement digital humanities (DH) data curation. Data curation is a method of managing research data that maintains the data’s accuracy and makes it available for reuse. It requires controlled data management.

The study uses a qualitative approach. Data collection was carried out through a focus group discussion in September–October 2022, interviews and document analysis. The informants came from four institutions in Indonesia.

The findings reveal that the national research repository has implemented data curation, albeit not optimally. Within the case study, one of the university repositories diligently curates its humanities data and has established networks extending to various ASEAN countries. Both the national archive repository and the other university repository have implemented rudimentary data curation practices but have not prioritized them. In conclusion, the readiness of the national research repository and the university repository stand at the high-capacity stage, while the national archive repository and the other university repository are at the established and early stages of data curation, respectively.

This study examined only four repositories due to time constraints. Nonetheless, the four institutions were able to provide a comprehensive picture of their readiness for DH data curation management.

This study provides insight into strategies for developing DH data curation activities in institutional repositories. It also highlights the need for professional development for curators so they can devise and implement stronger ownership policies and data privacy to support a data-driven research agenda.

This study describes the preparations that must be considered by institutional repositories in the development of DH data curation activities.

The Kingdom of Saudi Arabia’s dedicated pursuit of technological modernization positions it as a forefront leader in integrating advanced systems, aligning smoothly with the ambitious goals outlined in Vision 2030. The purpose of this study is to investigate the influence of integrating enterprise resource planning (ERP) and business intelligence (BI) systems on decision-making processes within the industrial sector of Saudi Arabia.

Using a quantitative research design, this study uses a bootstrapping approach and partial least squares structural equation modeling to meticulously analyze data collected from Saudi industrial firms.

The research reveals favorable relationships among infrastructure readiness, data quality, security and access control, user capabilities, user training and the integration of ERP and BI. These positive associations collectively affirm the overarching positive impact of ERP and BI integration on decision-making processes within the industrial sector.

The study underscores the strategic imperative of aligning organizational practices with the identified characteristics to fully unlock the potential benefits of ERP and BI integration in the Saudi Arabian industrial sector.

This study contributes significantly to the existing literature by delving into the integration of ERP and BI in the industrial sector and its nuanced impact on decision-making processes, specifically in the context of the Kingdom of Saudi Arabia – an area that has not been extensively studied.

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.