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The provision of high-quality e-Government services requires efficient and collaborative sharing of data across varied types of government agencies. However, interagency government data sharing (IDS) is not always spontaneous, active and unconditional. Adopting a stickiness theory, this paper reports on a research study, which explores the causes of data stickiness in IDS.

This study employed an inductive case study approach. Twenty-three officials from the government of City M in Hubei Province, Central China, were approached and interviewed using a semi-structured question script.

The analysis of the interview data pointed to 27 causes of data stickiness in five main themes: data sharing willingness; data sharing ability; data articulatability; data residence; and data absorptive capacity. The analysis revealed that interagency tensions and lack of preparedness of individual agencies are the main causes of data stickiness in IDS.

The case setting is based on China's Government, but the findings offer useful insights and indications that can be shared across international borders.

The strategic management literature emphasizes the concept of business intelligence (BI) as an essential competitive tool. Yet the sustainability of the firms’ competitive advantage provided by BI capability is not well researched. To fill this gap, this study attempts to develop a model for successful BI deployment and empirically examines the association between BI deployment and sustainable competitive advantage. Taking the telecommunications industry in Malaysia as a case example, the research particularly focuses on the influencing perceptions held by telecommunications decision makers and executives on factors that impact successful BI deployment. The research further investigates the relationship between successful BI deployment and sustainable competitive advantage of the telecommunications organizations. Another important aim of this study is to determine the effect of moderating factors such as organization culture, business strategy, and use of BI tools on BI deployment and the sustainability of firm’s competitive advantage.

This research uses combination of resource-based theory and diffusion of innovation (DOI) theory to examine BI success and its relationship with firm’s sustainability. The research adopts the positivist paradigm and a two-phase sequential mixed method consisting of qualitative and quantitative approaches are employed. A tentative research model is developed first based on extensive literature review. The chapter presents a qualitative field study to fine tune the initial research model. Findings from the qualitative method are also used to develop measures and instruments for the next phase of quantitative method. The study includes a survey study with sample of business analysts and decision makers in telecommunications firms and is analyzed by partial least square-based structural equation modeling.

The findings reveal that some internal resources of the organizations such as BI governance and the perceptions of BI’s characteristics influence the successful deployment of BI. Organizations that practice good BI governance with strong moral and financial support from upper management have an opportunity to realize the dream of having successful BI initiatives in place. The scope of BI governance includes providing sufficient support and commitment in BI funding and implementation, laying out proper BI infrastructure and staffing and establishing a corporate-wide policy and procedures regarding BI. The perceptions about the characteristics of BI such as its relative advantage, complexity, compatibility, and observability are also significant in ensuring BI success. The most important results of this study indicated that with BI successfully deployed, executives would use the knowledge provided for their necessary actions in sustaining the organizations’ competitive advantage in terms of economics, social, and environmental issues.

This study contributes significantly to the existing literature that will assist future BI researchers especially in achieving sustainable competitive advantage. In particular, the model will help practitioners to consider the resources that they are likely to consider when deploying BI. Finally, the applications of this study can be extended through further adaptation in other industries and various geographic contexts.

As libraries become more involved in curating research data, reference librarians will need to be trained in conducting data interviews with researchers to better understand their data and associated needs. This article seeks to identify and provide definitions for the basic terms and concepts of data curation for librarians to properly frame and carry out a data interview using the Data Curation Profiles (DCP) Toolkit.

The DCP Toolkit is a semi‐structured interview designed to assist librarians in identifying the data curation needs of researchers. The components of the DCP Toolkit were analyzed to determine the base level of knowledge needed for librarians to conduct effective data interviews. Specific concepts, definitions, and examples were sought through a review of articles, case studies, practitioner resources and from the experiences of the Purdue University Libraries.

Data curation concepts and terminology are not yet well‐defined and often vary across, or even within fields of study. This research informed the development of a workshop to train librarians in using the DCP Toolkit. The definitions and concepts addressed in the workshop include: data, data set, data lifecycle, data curation, data sharing, and roles for reference librarians.

Conducting a data interview can be a daunting task given the complexity of data curation and the lack of shared definitions. Practical tools and training are needed to help librarians develop capacity in data curation.

This article provides practical information for public service librarians to help them conceptualize and conduct a data interview with researchers.

Addresses the standardization of the measurements and the labels for concepts commonly used in the study of work organizations. As a reference handbook and research tool, seeks to improve measurement in the study of work organizations and to facilitate the teaching of introductory courses in this subject. Focuses solely on work organizations, that is, social systems in which members work for money. Defines measurement and distinguishes four levels: nominal, ordinal, interval and ratio. Selects specific measures on the basis of quality, diversity, simplicity and availability and evaluates each measure for its validity and reliability. Employs a set of 38 concepts ‐ ranging from “absenteeism” to “turnover” as the handbook’s frame of reference. Concludes by reviewing organizational measurement over the past 30 years and recommending future measurement reseach.

This chapter explains the research design. An interpretive methodology was considered most suitable for the study. Informed by an institutional framework, the interpretive methodology was selected for this monograph for its strengths of focusing on the research context, interactive processes, and meanings that are not measurable by quantitative approach. The interpretive methodology is also consistent with the ontological and epistemological positions of the researchers. Data were collected from interviewing four groups of key persons and a document survey. The data triangulation and multiple perspectives helped increase the reliability and validity of the study. Also, conducting data collection in a natural setting produced a rich data source. This enabled the provision of an enhanced understanding of the operation and effectiveness of corporate governance and financial reporting practice in a real setting. In addition, the systematic set of data analysis procedures helped improve research rigor and develop conceptual and theoretical understanding of issues of interest.

The purpose of this paper is to propose a systematic and rigorous process of data collection and fieldwork in qualitative research using four empirical studies of customer interactions in new product development (NPD) as examples. The intention is to dispel a misconception that the qualitative research lacks objectivity and methodological robustness.

To collect data for all the four studies a preliminary process of conducting fieldwork was first developed from the extant literature. This preliminary framework was applied in the first study and subsequently revised twice in the second and third study by incorporating necessary changes and additions. Finally, the framework was tested and further refined in the fourth study.

The findings from these four empirical qualitative studies have demonstrated that a theory generating idiographic research such as field interviews could be carried out systematically. These findings also provide a basis for proposing a structured framework for data collection.

The paper is based only on business‐to‐business NPD. Therefore, to increase the applicability of the results more studies are needed in other business and marketing fields.

The results offer an in‐depth look at specific research activities that can be carried out for efficient idea generation work and the overall NPD efforts.

The framework reported in this paper allows for an iterative data collection process from multiple respondents and from multiple sources. This method of data collection is a key issue because product managers tend to interact with the customers and other partners repeatedly and throughout the NPD process. Thus the product managers would find this framework useful for research involving NPD and customer interactions.