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This study addresses the need for a theory-guided, rich, descriptive account of research data repositories' (RDRs) understanding of data quality and the structures of their data quality assurance (DQA) activities. Its findings can help develop operational DQA models and best practice guides and identify opportunities for innovation in the DQA activities.

The study analyzed 122 data repositories' applications for the Core Trustworthy Data Repositories, interview transcripts of 32 curators and repository managers and data curation-related webpages of their repository websites. The combined dataset represented 146 unique RDRs. The study was guided by a theoretical framework comprising activity theory and an information quality evaluation framework.

The study provided a theory-based examination of the DQA practices of RDRs summarized as a conceptual model. The authors identified three DQA activities: evaluation, intervention and communication and their structures, including activity motivations, roles played and mediating tools and rules and standards. When defining data quality, study participants went beyond the traditional definition of data quality and referenced seven facets of ethical and effective information systems in addition to data quality. Furthermore, the participants and RDRs referenced 13 dimensions in their DQA models. The study revealed that DQA activities were prioritized by data value, level of quality, available expertise, cost and funding incentives.

The study's findings can inform the design and construction of digital research data curation infrastructure components on university campuses that aim to provide access not just to big data but trustworthy data. Communities of practice focused on repositories and archives could consider adding FAIR operationalizations, extensions and metrics focused on data quality. The availability of such metrics and associated measurements can help reusers determine whether they can trust and reuse a particular dataset. The findings of this study can help to develop such data quality assessment metrics and intervention strategies in a sound and systematic way.

To the best of the authors' knowledge, this paper is the first data quality theory guided examination of DQA practices in RDRs.

Joon Y. Park is one of the pioneers in developing nonlinear cointegrating regression. Since his initial work with Phillips (Park & Phillips, 2001) in the area, the past two decades have witnessed a surge of interest in modeling nonlinear nonstationarity in macroeconomic and financial time series, including parametric, nonparametric and semiparametric specifications of such models. These developments have provided a framework of econometric estimation and inference for a wide class of nonlinear, nonstationary relationships. In honor of Joon Y. Park, this chapter contributes to this area by exploring nonparametric estimation of functional-coefficient cointegrating regression models where the structural equation errors are serially dependent and the regressor is endogenous. The self-normalized local kernel and local linear estimators are shown to be asymptotic normal and to be pivotal upon an estimation of co-variances. Our new results improve those of Cai et al. (2009) and open up inference by conventional nonparametric method to a wide class of potentially nonlinear cointegrated relations.

This chapter develops an asymptotic theory for a general transformation model with a time trend, stationary regressors, and unit root nonstationary regressors. This model extends that of Han (1987) to incorporate time trend and nonstationary regressors. When the transformation is specified as an identity function, the model reduces to the conventional cointegrating regression, possibly with a time trend and other stationary regressors, which has been studied in Phillips and Durlauf (1986) and Park and Phillips (1988, 1989). The limiting distributions of the extremum estimator of the transformation parameter and the plug-in estimators of other model parameters are found to critically depend upon the transformation function and the order of the time trend. Simulations demonstrate that the estimators perform well in finite samples.

With the increasing importance of technology in hospitality and tourism, technology-driven service innovation has been a salient topic discussed from both customers’ and suppliers’ perspectives. However, there has been a lack of research that provides an overview of research on technology-driven service innovation. The purpose of this study is to review current discussions on technology-driven service innovation and provide directions for future studies in the hospitality and tourism literature.

A total of 82 articles on technology-driven service innovation were collected from top-tier hospitality and tourism journals. The papers were analyzed using content analysis to derive key topics discussed in the literature. Such discussions were made by different service innovation categories, antecedents, outcomes and theories. Future research agendas were suggested based on the research gap found in the literature.

The results indicate that prior discussions on technology-driven service innovation viewed technology as a service or service delivery method, with limited focus on management, marketing and institutional service innovation. In addition, the study reveals five key topics that need further discussion, such as cocreative technology, human resources management, strategy management, emerging technology and digital transformation.

While there have been increasing studies that reveal determining roles of technology in service innovation, scarce research introduced the new concept of technology-driven service innovation, suggesting a comprehensive approach. By adopting the unique approach of technology-driven service innovation, the research reveals the multifaceted roles of technology in service innovation and areas that need further discussion to implement highly sustainable strategies.

The research adds to the knowledge of technology-driven service innovation by providing a holistic view of current discussions, finding research gaps and proposing future research agendas for extended discussion.

In this chapter, the authors contribute toward building a better understanding of farmers’ responses to behavioral drivers of land-use decision by establishing an alternative analytical procedure, which can overcome various drawbacks suffered by methods currently used in existing studies. Firstly, our procedure makes use of spatially high-resolution data, so that idiosyncratic effects of physical environment drivers, e.g., soil textures, can be explicitly modeled. Secondly, we address the well-known censored data problem, which often hinders a successful analysis of land-use shares. Thirdly, we incorporate spatial error dependence (SED) and heterogeneity in order to obtain efficiency gain and a more accurate formulation of variances for the parameter estimates. Finally, the authors reduce the computational burden and improve estimation accuracy by introducing an alternative generalized method of moments (GMM)–quasi maximum likelihood (QML) hybrid estimation procedure. The authors apply the newly proposed procedure to spatially high-resolution data in England and found that, by taking these features into consideration, the authors are able to formulate conclusions about causal effects of climatic and physical environment, and environmental policy on land-use shares that differ significantly from those made based on methods that are currently used in the literature. Moreover, the authors show that our method enables derivation of a more effective predictor of the land-use shares, which is utterly useful from the policy-making point of view.

This chapter considers the estimation of a parametric single-index predictive regression model with integrated predictors. This model can handle a wide variety of non-linear relationships between the regressand and the single-index component containing either the cointegrated predictors or the non-cointegrated predictors. The authors introduce a new estimation procedure for the model and investigate its finite sample properties via Monte Carlo simulations. This model is then used to examine stock return predictability via various combinations of integrated lagged economic and financial variables.

This study aims to investigate the key factors that influence the behavioural intention of doctors to adopt the knowledge sharing driven blockchain technology in government hospitals. The study is based on the Unified Theory of Acceptance and Use of Technology 2, with the addition of trust as an independent variable and knowledge sharing as a mediating variable between trust and behavioural intention.

The data for the study was collected through a correlation and cross-sectional study using a survey, with a sample of 322 responses being used for the final analysis. The initial analysis of the data was conducted using SPSS v.26, followed by a partial least squares structural equation modelling (PLS-SEM) analysis using SmartPLS v.3.9 to test the validity and reliability of the measures and to examine the hypothesized relationships.

The results supported the proposed framework. The results of PLS-SEM indicate that all proposed pathways support the model. In particular, the results of the study reveal that performance expectation, effort expectation, social influence, facilitation conditions and trust are drivers of blockchain adoption and have a significant impact on the behavioural intention of clinicians in hospitals. Furthermore, the study found that knowledge sharing mediated the relationship between trust and behavioural intention.

The present study sheds light on the challenges facing blockchain technology, such as privacy and trust concerns and proposes a more sustainable approach based on knowledge management to enhance the effectiveness of blockchain technology and overcome these challenges.

The significance of this paper lies in the limited literature examining the relationships between blockchain technology and knowledge management processes. Furthermore, a hypothetical framework that includes the knowledge sharing process as a mediating variable between trust and behavioural intention to adopt blockchain technology has not been presented or developed in any previous studies, particularly in the context of Iraq. Thus, this work is novel and unique in its approach.

The purpose of this study is to introduce new tools to develop a more precise and focused bibliometric analysis on the field of digitalization in healthcare management. Furthermore, this study aims to provide an overview of the existing resources in healthcare management and education and other developing interdisciplinary fields.

This work uses bibliometric analysis to conduct a comprehensive review to map the use of the unified theory of acceptance and use of technology (UTAUT) and the unified theory of acceptance and use of technology 2 (UTAUT2) research models in healthcare academic studies. Bibliometric studies are considered an important tool to evaluate research studies and to gain a comprehensive view of the state of the art.

Although UTAUT dates to 2003, our bibliometric analysis reveals that only since 2016 has the model, together with UTAUT2 (2012), had relevant application in the literature. Nonetheless, studies have shown that UTAUT and UTAUT2 are particularly suitable for understanding the reasons that underlie the adoption and non-adoption choices of eHealth services. Further, this study highlights the lack of a multidisciplinary approach in the implementation of eHealth services. Equally significant is the fact that many studies have focused on the acceptance and the adoption of eHealth services by end users, whereas very few have focused on the level of acceptance of healthcare professionals.

To the best of the authors’ knowledge, this is the first study to conduct a bibliometric analysis of technology acceptance and adoption by using advanced tools that were conceived specifically for this purpose. In addition, the examination was not limited to a certain era and aimed to give a worldwide overview of eHealth service acceptance and adoption.