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We analyze and assess new evidence on employment dynamics from a new data source – the National Establishment Time Series (NETS). The NETS offers advantages over existing data sources for studying employment dynamics, including tracking business establishment relocations that can contribute to job creation or destruction on a regional level. Our primary purpose in this paper is to assess the reliability of the NETS data along a number of dimensions, and we conclude that it is a reliable data source although not without limitations. We also illustrate the usefulness of the NETS data by reporting, for California, a full decomposition of employment change into its six constituent processes, including job creation and destruction stemming from business relocation, which has figured prominently in policy debates but on which there has been no systematic evidence.

Mashups have been studied extensively in the literature; nevertheless, the large body of work in this area focuses on service/data level integration and leaves UI level integration, hence UI mashups, almost unexplored. The latter generates digital environments in which participating sources exist as individual entities; member applications and data sources share the same graphical space particularly in the form of widgets. However, the true integration can only be realized through enabling widgets to be responsive to the events happening in each other. The authors call such an integration “widget orchestration” and the resulting application “mashup by orchestration”. This article aims to explore and address challenges regarding the realization of widget‐based UI mashups and UI level integration, prominently in terms of widget orchestration, and to assess their suitability for building web‐based personal environments.

The authors provide a holistic view on mashups and a theoretical grounding for widget‐based personal environments. The authors identify the following challenges: widget interoperability, end‐user data mobility as a basis for manual widget orchestration, user behavior mining – for extracting behavioral patterns – as a basis for automated widget orchestration, and infrastructure. The authors introduce functional widget interfaces for application interoperability, exploit semantic web technologies for data interoperability, and realize end‐user data mobility on top of this interoperability framework. The authors employ semantically enhanced workflow/process mining techniques, along with Petri nets as a formal ground, for user behavior mining. The authors outline a reference platform and architecture that is compliant with the authors' strategies, and extend W3C widget specification respectively – prominently with a communication channel – to foster standardization. The authors evaluate their solution approaches regarding interoperability and infrastructure through a qualitative comparison with respect to existing literature, and provide a computational evaluation of the behavior mining approach. The authors realize a prototype for a widget‐based personal learning environment for foreign language learning to demonstrate the feasibility of their solution strategies. The prototype is also used as a basis for the end‐user assessment of widget‐based personal environments and widget orchestration.

The evaluation results suggest that the interoperability framework, platform, and architecture have certain advantages over existing approaches, and the proposed behavior mining techniques are adequate for the extraction of behavioral patterns. User assessments show that widget‐based UI mashups with orchestration (i.e. mashups by orchestration) are promising for the creation of personal environments as well as for an enhanced user experience.

This article provides an extensive exploration of mashups by orchestration and their role in the creation of personal environments. Key challenges are described, along with novel solution strategies to meet them.

Most local governance assessment tools are entirely or partially based on stakeholders’ surveys, focus groups and benchmarks of different local governments in the world. These tools remain a subjective way of local governance evaluation. To measure the performance of local good-governance using an unbiased assessment technique, the authors have developed a framework to help automate the design process of a data warehouse (DW), which provides local and central decision-makers with factual, measurable and accurate local government data to help assess the performance of local government. The purpose of this paper is to propose the extraction of the DW schema based on a mixed approach that adopts both i* framework for requirements-based representation and domain ontologies for data source representation, to extract the multi-dimensional (MD) elements. The data was collected from various sources and information systems (ISs) deployed in different municipalities.

The authors present a framework for the design and implementation of a DW for local good-governance assessment. The extraction of facts and dimensions of the DW’s MD schema is done using a hybrid approach, where the extraction of requirement-based DW schema and source-based DW schema are done in parallel followed by the reconciliation of the obtained schemas to obtain the good-governance assessment DW final design.

The authors developed a novel framework to design and implement a DW for local good-governance assessment. The framework enables the extraction of the DW MD schema by using domain ontologies to help capture semantic artifacts and minimize misconceptions and misunderstandings between different stakeholders. The introduction and use of domain ontologies during the design process serves the generalization and automation purpose of the framework.

The presently conducted research faced two main limitations as follows: the first is the full automation of the design process of the DW and the second, and most important, is access to local government data as it remains limited because of the lack of digitally stored data in municipalities, especially in developing countries in addition to the difficulty of accessing the data because of regulatory aspects and bureaucracy.

The local government environment is among the public administrations most subject to change-adverse cultures and where the authors can face high levels of resistance and significant difficulties during the implementation of decision support systems, despite the commitment/engagement of decision-makers. Access to data sources stored by different ISs might be challenging. While approaching the municipalities for data access, it was done in the framework of a research project within one of the most notorious universities in the country, which gave more credibility and trust to the research team. There is also a need for further testing of the framework to reveal its scalability and performance characteristics.

Compared to other local government assessment ad hoc tools that are partially or entirely based on subjectively collected data, the framework provides a basis for automated design of a comprehensive local government DW using e-government domain ontologies for data source representation coupled with the goal, rationale and business process diagrams for user requirements representations, thus enabling the extraction of the final DW MD schema.

The purpose of this paper is to derive a taxonomy of business models used by start-up firms that rely on data as a key resource for business, namely data-driven business models (DDBMs). By providing a framework to systematically analyse DDBMs, the study provides an introduction to DDBM as a field of study.

To develop the taxonomy of DDBMs, business model descriptions of 100 randomly chosen start-up firms were coded using a DDBM framework derived from literature, comprising six dimensions with 35 features. Subsequent application of clustering algorithms produced six different types of DDBM, validated by case studies from the study’s sample.

The taxonomy derived from the research consists of six different types of DDBM among start-ups. These types are characterised by a subset of six of nine clustering variables from the DDBM framework.

A major contribution of the paper is the designed framework, which stimulates thinking about the nature and future of DDBMs. The proposed taxonomy will help organisations to position their activities in the current DDBM landscape. Moreover, framework and taxonomy may lead to a DDBM design toolbox.

This paper develops a basis for understanding how start-ups build business models capture value from data as a key resource, adding a business perspective to the discussion of big data. By offering the scientific community a specific framework of business model features and a subsequent taxonomy, the paper provides reference points and serves as a foundation for future studies of DDBMs.

Aware that “those who aren't counted don't count” in health program planning, a community coalition, called African Partnership for Health, attempts a current estimate of the African community living in Portland, Oregon, USA. This paper seeks to describe the findings.

The paper's definition of the “African community” was crucially informed by community participation in the research process. The authors drew on existing publicly available data sources to estimate the size of the target population and identified the strengths and weaknesses of each source.

Conservative estimations are of a 2010 African community population of 11,500‐15,500 for the Portland metropolitan area. No data source on its own would have resulted in this estimate.

Areas for further research include creating practical systems to collect data on country of origin and to address an existing data bias towards refugees over immigrants. In the USA, more robust data collection systems are needed to estimate the impact of secondary migration on the size and characteristics of refugee and immigrant communities.

Health program planners should be aware that existing data may include more information about some groups (refugees as opposed to immigrants) and emphasize some characteristics (race as opposed to country of origin).

Including immigrant and refugee community members in the research process can result in more relevant definition of that community, which may lead to more effective program targeting and design.

As the health care sector is changing rapidly, there is a growing need to develop new ways to make data-driven decisions, especially at the organizational level. Data utilization, like business intelligence (BI) activities, benefits health care organizations. The purpose of this paper is to study the potential of Big Data and the utilization of BI tools in creating value in the private health care industry in Finland.

Intellectual capital (IC) components and Möller et al.’s (2005) work on value capabilities are used as a framework to point out the roles of data utilization and BI tools in value creation. Thematic interviews enable understanding of the value creation based on Big Data potential and utilization of BI tools in the Finnish private health care industry.

The findings will provide an understanding of the existing data sources and BI tools used in private health care. In addition, it provides an insight into the future-oriented Big Data potential, which can create new business concepts. The approach provides valuable insights for value identifying the future needs of data utilization and creates an understanding on the current state within the private health care sector.

Data-driven value creation is one of the most discussed topics in private health care sector. By analyzing the current data-source utilization, challenges with data and BI tool utilization and the future vision and development roadmaps, the authors gain a better understanding of the IC components and value creation capabilities.

In order to predict the future we must examine the past in order to observe trends over periods of time and establish the degree of probability with which these trends are likely to repeat themselves in the future. All forecasts are wrong, and management must be aware of this fact and decide upon the degree of inexactitude that can be tolerated when planning for the future.

The purpose of this study is to explore an optimal research design for research on women entrepreneurs involved in negotiating term sheets for private equity capital. This research explores new ways for researchers to connect with such current “invisibles” through the use of a mixed method and mixed mode research design to expand sampling options and secure respondent participation. The authors discuss existing data sets that have been used as secondary sources for data on financing of companies and consider their inadequacy for research questions about process issues in negotiation. The authors present process-related findings regarding the efficacy of the research design.

This paper reviews research on research methodology, incorporating a discussion of practices outside of the entrepreneurship discipline to discover effective practices for identifying respondents and data not currently captured in entrepreneurship data sources. The respondents were found through social media sites, angel networks, University networks and via identification through a proprietary financial intelligence database.

An optimal research design to identify women business owners of growth-oriented firms who have negotiated private equity should consider mixed methods designs and mixed modes, including the use of digital networks that signal to potential respondents that research is being done.

Although the authors developed the multi-method, mixed mode (MMMM) research design, the sample size is still relatively small. This raises concerns about generalizability to the larger population and limits statistical analysis more suitable with larger data sets. However, the MMMM research design has enabled the authors to reach a difficult target sample. It has proven effective, although a longer time frame would have been helpful.

All of the large scale databases in entrepreneurship have limitations in providing optimal sampling frames for process-related research. The present research study was able to use conventional networks, social media sites and angel networks to connect with women business owners who have raised private equity, but who lack visibility in current data sets. The study shows that through the use of multiple methods, women entrepreneurs can be researched and some will share their experiences about process issues. The sample size was small and the quantitative data cannot be generalized. However, the methodology works and allows researchers to explore experiences that are not captured in existing data sets.

Entrepreneurship researchers can connect with “invisibles” by becoming more “social” and using social media sites that are used by women entrepreneurs. Researchers may not have immediate access to women entrepreneurs through these means, but rather they need to develop interpersonal contacts, build a social presence and trust to recruit respondents to complete online questionnaire studies about substantive topics such as negotiating term sheets for equity investments in their companies.

This paper summarizes the “research on research methodologies” in entrepreneurship, reviews secondary data sources and discusses their limitations for specific types of research questions. A review of the value of MMMM research designs and best practices in online survey research outside of entrepreneurship provides insights into the incorporation of digital tools in other disciplines.

The purpose of this paper is to describe the benefits of integrating personalization within a library web site and presents methodology for achieving this goal within an academic setting.

The project documented in this study explores the use of student course enrollment data as the basis for creating a personalized library web site. Off-the-shelf, open source applications are used in conjunction with existing university data to deliver a final product that offers an enhanced user experience for the university community.

Adaptive personalization is increasingly commonplace on the web. Academic libraries have a unique source of existing data that offers the potential of adding personalization to the library web site. At present, the personalization of library online services remains largely unexplored. This project illustrates one relatively low-cost method to help libraries interested in creating personalized web sites.

This paper provides a guide for libraries interested in the implementation of personalization within their web sites.

The project described in this case study is highly unique within libraries. The paper outlines the feasibility and technical requirements associated with using course enrollment data to add personalized content to a library web site.

Generally, the user requires customized information reflecting his/her current needs and interests that are stored in his/her profile. There are many sources which may provide beneficial information to enrich the user’s interests such as his/her social network for recommendation purposes. The proposed approach rests basically on predicting the reliability of the users’ profiles which may contain conflictual interests. The paper aims to discuss this issue.

This approach handles conflicts by detecting the reliability of neighbors’ profiles of a user. The authors consider that these profiles are dependent on one another as they may contain interests that are enriched from non-reliable profiles. The dependency relationship is determined between profiles, each of which contains interests that are structured based on k-means algorithm. This structure takes into consideration not only the evolutionary aspect of interests but also their semantic relationships.

The proposed approach was validated in a social-learning context as evaluations were conducted on learners who are members of Moodle e-learning system and Delicious social network. The quality of the created interest structure is assessed. Then, the result of the profile reliability is evaluated. The obtained results are satisfactory. These results could promote recommendation systems as the selection of interests that are considered of enrichment depends on the reliability of the profiles where they are stored.

Some specific limitations are recorded. As the quality of the created interest structure would evolve in order to improve the profile reliability result. In addition, as Delicious is used as a main data source for the learner’s interest enrichment, it was necessary to obtain interests from other sources, such as e-recruitement systems.

This research is among the pioneer papers to combine the semantic as well as the hierarchical structure of interests and conflict resolution based on a profile reliability approach.