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This survey-based study aims to explore the research data dissemination and requesting practices of Arab researchers. It investigates the reasons, types, methods, barriers and motivations associated with data sharing and requesting in the Arab research community.

A cross-sectional survey was conducted with 205 Arab researchers representing various disciplines and career stages. Descriptive statistics were used for data analysis.

The study found that 91.2% of Arab researchers share data, while 56.6% access data from others. Reasons for sharing include promoting transparency and collaboration while requesting data is driven by the need to validate findings and explore new research questions. Processed/analysed data and survey/questionnaire data are the most commonly shared and requested types.

This study contributes to the literature by examining data sharing and requesting practices in the Arab research community. It provides original insights into the motivations, barriers and data types shared and requested by Arab researchers. This can inform future research and initiatives to promote regional data sharing.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2023-0283

This paper aims to increase understanding of pertinent exogenous and endogenous antecedents that can reduce data privacy breaches.

A cross-sectional survey was used to source participants' perceptions of relevant exogenous and endogenous antecedents developed from the Antecedents-Privacy Concerns-Outcomes (APCO) model and Social Cognitive Theory. A research model was proposed and tested with empirical data collected from 213 participants based in Canada.

The exogenous factors of external privacy training and external privacy self-assessment tool significantly and positively impact the study's endogenous factors of individual privacy awareness, organizational resources allocated to privacy concerns, and group behavior concerning privacy laws. Further, the proximal determinants of data privacy breaches (dependent construct) are negatively influenced by individual privacy awareness, group behavior related to privacy laws, and organizational resources allocated to privacy concerns. The endogenous factors fully mediated the relationships between the exogenous factors and the dependent construct.

This study contributes to the budding data privacy breach literature by highlighting the impacts of personal and environmental factors in the discourse.

The results offer management insights on mitigating data privacy breach incidents arising from employees' actions. Roles of external privacy training and privacy self-assessment tools are signified.

Antecedents of data privacy breaches have been underexplored. This paper is among the first to elucidate the roles of select exogenous and endogenous antecedents encompassing personal and environmental imperatives on data privacy breaches.

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.

Data transformation has prompted enterprises to rethink their strategic development. Scholars have frequently acknowledged the vast potential value of supply chain data and realised that simply owning data resources cannot guarantee excellent innovation performance (IP). Therefore, this study focussed on the mediating and moderating issues between data-driven supply chain orientation (DDSCO) and IP. More specifically, the purpose was to explore (1) whether DDSCO promotes enterprise innovation through dynamic and improvisational capabilities and (2) how information complexity (INC) plays a moderating role between capabilities and performance.

An empirical study was performed using the results of a questionnaire survey, and a literature review was used to build the premises of this study. A sample was conducted on 296 Chinese enterprises, and the data collected were used to test the hypothesis by successive regression.

This research has implications for the theoretical development of DDSCO, as well as the dynamic capabilities (DC) and improvisation capabilities (IC) in innovation strategic literature. The empirical results show that DDSCO has a direct, positive impact on both DC and IC, which thus positively impact IP. Meanwhile, IC has a negative moderating effect on the path joining DC and IP. Conversely, IC has a positive moderating effect on the path joining IC and IP.

Although this study has limitations, it also creates opportunities for future research. The survey comes from different industries, so the possibility of unique influences within industries cannot be ruled out. Second, the authors' survey is based on cross-sectional data, which allow for more comprehensive data verification in the future. Third, this study also provides opportunities for future research, because it proves that DC and IC, as partial mediators of DDSCO and IP, can mine other paths of the data-driven supply chain in IP. For example, the perspective of the relationship between supply chain members, knowledge perspective, etc.

The research findings offer a novel perspective for enterprise managers. First, enterprises can leverage supply chain data to gain competitive advantages in innovation. Second, it is imperative for enterprises to acknowledge the significance of developing dynamic and IC. This also requires enterprises to acknowledge innovations in DDSCO necessitate a focus on dynamic and IC. Third, it is recommended that managers take into account both sides of IC and encourage enterprises to prioritise the utilisation of IC.

Empirical research results revealed how DDSCO improves IP and is an extension of digital transformation in the supply chain field, providing new opportunities and challenges for enterprise innovation. It can also expand the enterprise's understanding of DDSCO. Second, based on resource-based theory, it is possible to develop and test theoretical arguments regarding the importance of dynamic and IC as intermediaries in the DDSCO-IP. Third, the authors conducted simulations of highly dynamic data environments to develop and test theoretical arguments about the importance of IC as a moderator of capabilities-performance relationships.

Effective use of data across public health organisations (PHOs) is essential for the provision of health services. While health technology and data use in clinical practice have been investigated, interactions with data in non-clinical practice have been largely neglected. The purpose of this paper is to consider what constitutes data, and how people in non-clinical roles in a PHO interact with data in their practice.

This mixed methods study involved a qualitative exploration of how employees of a large PHO interact with data in their non-clinical work roles. A quantitative survey was administered to complement insights gained through qualitative investigation.

Organisational boundaries emerged as a defining issue in interactions with data. The results explain how data work happens through observing, spanning and shifting of boundaries. The paper identifies five key issues that shape data work in relation to boundaries. Boundary objects and processes are considered, as well as the roles of boundary spanners and shifters.

The study was conducted in a large Australian PHO, which is not completely representative of the unique contexts of similar organisations. The study has implications for research in information and organisational studies, opening fields of inquiry for further investigation.

Effective systems-wide data use can improve health service efficiencies and outcomes. There are also implications for the provision of services by other health and public sectors.

The study contributes to closing a significant research gap in understanding interactions with data in the workplace, particularly in non-clinical roles in health. Research analysis connects concepts of knowledge boundaries, boundary spanning and boundary objects with insights into information behaviours in the health workplace. Boundary processes emerge as an important concept to understand interactions with data. The result is a novel typology of interactions with data in relation to organisational boundaries.

The integration of Lean Six Sigma (LSS) and Industry 4.0 (I4.0) is in the nascent stage and promises to achieve new optimums in operational excellence. This study aims to empirically examine the enablers, barriers, benefits and application of I4.0 technologies in LSS and I4.0 integration.

A pilot survey was chosen as an appropriate methodology, as LSS and I4.0 integration is still budding. The survey targeted senior quality management professionals, quality managers, team leaders, LSS Black Belts and operations managers to collect the relevant research data. The questionnaire was sent to 200 respondents and received 53 valid responses.

This study reveals that “top management support” is an essential enabler for LSS and I4.0 integration. The most significant barrier was “poor understanding of data analysis” and “lack of top management support”. The findings further illustrated that LSS and I4.0 integration resulted in greater efficiency, lower operational costs, improved productivity, improved customer satisfaction and improved quality. Regarding I4.0 technology integration at different phases of LSS, the authors noticed that big data analytics and artificial intelligence (AI) are the most prominent technologies used in all phases of LSS implementation.

One of the limitations of this study is the sample size. LSS and I4.0 are emerging concepts; hence, obtaining a larger sample size is difficult. In addition, the study used non-parametric tests to analyse the data. Therefore, future studies should be conducted with large sample sizes across different continents and countries to understand differences in the key findings.

The outcomes of this study can be useful for organisational managers to understand the enablers and barriers before integrating LSS and I4.0 for adoption in their organisations. Secondly, it helps to convince top management and human resource personnel by providing a list of benefits of LSS and I4.0 integration. Finally, it can help decision-makers understand which I4.0 technologies can be used in different stages of LSS methodology.

LSS and I4.0 integration was studied at a conceptual level. This is the first empirical study targeted toward understanding the LSS and I4.0 integration. In addition, this study investigates the application of widely used I4.0 technologies in different phases of LSS.

This study is exploratory in nature and designed to address poorly documented issues in the literature. The dimensions of regional distribution or spatial organisation of Industry 4.0 (I4.0), including the potential role of clusters, have only recently been addressed, with most available studies focusing on advanced, mainly Western European countries. Although developing fast, the literature on I4.0 in other countries, such as the Central and Eastern European or post-transition economies like Poland, needs to pay more attention to the spatial distribution or geographical and organisational aspects. In response to the identified knowledge gap, this paper aims to identify the role of clusters in the transformation towards I4.0. This explains why clusters may matter for advancing the fourth digital transformation, how advanced in implementing I4.0 solutions are the residents of Polish clusters and how they perceive the advantages of cluster membership for such implementation. Finally, it seeks to formulate policy recommendations based on the evidence gathered.

The methodology used in this study combines quantitative analysis of secondary data from a cluster benchmarking survey with a case study approach. The benchmarking survey, conducted by the polish agency for enterprise development in 2021, gathered responses from 435 cluster members and 41 cluster managers, representing an estimated 57% of the current clusters in Poland. In addition to quantitative analysis, a case study approach was used, incorporating primary sources such as interview with cluster managers and surveys of cluster members, as well as secondary sources like company documents and information from cluster organisation websites. Statistical analysis involved assessing the relationship between technology implementation and the adoption of management systems, as well as exploring potential correlations between technology use and company characteristics such as revenue, export revenue share and number of employees using Pearson correlation coefficient.

In Poland, implementing I4.0 technologies by cluster companies is still modest. The cluster has influenced the use of I4.0 technologies in 23% of surveyed companies. Every second surveyed company declared a positive impact of a cluster on technological advancement. The use of I4.0 technologies is not correlated with the revenue of clustered companies. A rather bleak picture emerges from the results, revealing a need for more interest among cluster members in advancing I4.0 technologies. This may be due to a comfortable situation in which firms still enjoy alternative competitive advantages that do not force them to seek new advanced advantages brought about by I4.0. It also reflects the sober approach and awareness of associated high costs and necessary investments, which are paramount and prevent successful I4.0 implementation.

The limitations inherent in this study reflect the scarcity of the available data. This paper draws on the elementary survey administered centrally and is confined by the type of questions asked. The empirical section focuses on an important, though only one selected sector of the economy – the automotive industry. Nevertheless, the diagnosis of the Polish cluster’s role in advancing I4.0 should complement the existing literature.

The exploratory study concludes with policy recommendations and sets the stage for more detailed studies. Amidst the research’s limitations, this study pioneers a path for future comprehensive investigations, enabling a deeper understanding of Polish clusters’ maturity in I4.0 adoption. By comparing the authors’ analysis of the Polish Automotive Group (PGM) cluster with existing literature, the authors uncover a distinct disparity between the theoretical prominence of cluster catalysis and the current Polish reality. Future detailed dedicated enquiries will address these constraints and provide a more comprehensive map of Polish clusters’ I4.0 maturity.

This study identifies patterns of I4.0 implementation and diagnoses the role of clusters in the transformation towards I4.0. It investigates how advanced is the adoption of I4.0 solutions among the residents of Polish clusters and how they perceive the advantages of cluster membership for such transformation. Special attention was paid to the analysis of the automotive sector. Comparing the conclusions drawn from the analysis of the Polish PGM cluster in this case study to those from the literature on the subject, it becomes clear that the catalytic role of clusters in the implementation of I4.0 technologies by enterprises, as emphasised in the literature, is not yet fully reflected in the Polish reality.

Data librarianship, or data-driven librarianship, is the combination of information science, data science and e-science fields and is gaining gradual importance in the library and information science (LIS) profession. Hence, this study investigates the data librarianship core concepts (motivational factors, challenges, skills and appropriate training platforms) to learn and successfully launch data librarianship services.

A survey method was used and the data were collected through online questionnaire. Purposive sampling method was applied and 132 responses were received with 76 respondents from the public and 56 from the private sector universities of Pakistan. The statistical package for social sciences (SPSS version 25) was used, and descriptive and inferential statistics were applied to analyzed the data.

LIS professionals understand the importance of data-driven library services and perceive that such services are helpful in evolving the image of the library, helping with the establishment of institutional data repositories/data banks, developing data resources and services for library patrons and especially researchers, and receiving appreciation and acknowledgment from the higher authorities. The major challenges that emerged from the data were: missing data policies, limited training opportunities for data librarianship roles, no additional financial benefits, lack of infrastructure and systems, lack of organizational support for the initiation of data-driven services, and lack of skills, knowledge and expertise. Data librarianship is in its early stages in Pakistan, and consequently, the LIS professionals are lacking basic, advanced and technical data-driven skills.

The policy, theoretical and practical implications describe an immediate need for framing data policies. Such policies will help the libraries or any other relevant entities to store the data and assign metadata and documentation in such a way that it is easy to retrieve and reusable for others.

This is the first study in Pakistan to investigate the perceptions of LIS professionals about data librarianship core concepts: motivational factors, challenges, skills and appropriate training platforms to grasp data-driven skills and successfully launch library services.

This study aims to evaluate the quality of information recorded in Behaviour Monitoring Charts (BMC) for Behaviours that Challenge (BtC) in dementia in an older adult inpatient dementia service in the North of England (Aim I) and to understand staff perceptions and experiences of completing BMC for BtC in dementia (Aim II).

Descriptive statistics and graphs were used to analyse and interpret quantitative data gathered from BMC (Aim I) and Likert-scale survey responses (Aim II). Thematic analysis (Braun and Clarke, 2006) was used to analyse and interpret qualitative data collected from responses to open-ended survey questions and, separately, focus group discussions (Aim II).

Analysis of the BMCs revealed that some of the data recorded relating to antecedents, behaviours and consequences lacked richness and used vague language (i.e. gave reassurance), which limited its clinical utility. Overall, participants and respondents found BMC to be problematic. For them, completing BMCs were not viewed as worthwhile, the processes that followed their completion were unclear, and they left staff feeling disempowered in the systemic hierarchy of an inpatient setting.

Functional analysis of BMC helps identify and inform appropriately tailored interventions for BtC in dementia. Understanding how BMCs are used and how staff perceive BMC provides a unique opportunity to improve them. Improving BMC will support better functional analysis of BtC, thus allowing for more tailored interventions to meet the needs of people with dementia.

Achieving tenure security is a global challenge impacting cities of the global south. The purpose of this paper is to evaluate the role of technology-enabled solutions as an enabler for the tenure rights of slum dwellers.

In this paper, we adopted a case study approach to analyze the use cases for technologies aiding India’s securitization of land tenure. The flagship state mission of Odisha, named the Jaga Mission, and that of Punjab, named BASERA – the Chief Minister’s Slum Development Program – were used as cases for this paper.

It was found that technologies like drone imagery and digital surveys fast-tracked the data collection and helped in mapping the slums with accuracy, mitigating human errors arising during measurement – a necessary condition for ensuring de jure tenure security. The adoption of a technology-based solution, along with a suitable policy and legal framework, has helped in the distribution of secure land titles to the slum dwellers in these states.

Odisha’s and Punjab’s journey in using technology to enable tenure security for its urban poor residents can serve as a model for the cities of the global south, dealing with the challenges of providing secure tenure and property rights.