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Access to unbiased self-reported (primary) data for a normative concept like social sustainability has been a challenge for construction project management (CPM) scholars, and this difficulty has been further amplified by the ongoing COVID-19 pandemic. This paper aims to address this issue by asserting the suitability of secondary data as a methodologically sound but underutilized alternative and providing directions for secondary data-based research on social sustainability in a project setting.

By drawing on a framework for social sustainability and using “project-as-practice” approach as its point of departure, this conceptual paper identifies possibilities for utilizing multiple secondary sources in CPM research.

The paper provides a roadmap for identification of secondary sources, access to data, potential research designs and methods, limitations of and cautions in using secondary sources, and points to many novel lines of empirical enquiries to stimulate secondary data-based research on social sustainability in CPM.

Indicated secondary sources and empirical opportunities can support research efforts that aim to promote societal welfare through construction projects.

The presented guidance will assist researchers in identifying, accessing and utilizing naturalistic, secondary data for designing and conducting empirical research that cuts across social sustainability and CPM. This, in turn, will facilitate methodological pluralism and “practice turn” in such research endeavors.

The purpose of this paper is to identify the dividend policy determinants of Sri Lankan firms and why they pay dividends.

The study uses several quantitative approaches to investigate dividend determinants using market (secondary) data of 190 Sri Lankan firms and 1,330 firm-year observations. Dividend determinants are also identified using survey (primary) data from 141 of the 190 firms. Triangulation is then used to facilitate validation of the data through cross-verification from two data sources.

Analysis of the market data reveals that firm size, industry impact, corporate governance, free cash flow, earnings, past dividends, profitability, investment opportunities, net working capital, concentrated ownership structure and investor preference represent the most important dividend determinants. Survey data confirm these findings. The evidence supports the pecking order, signaling, free cash flow, catering and outcome theories using both secondary and primary data and the bird-in-the-hand theory using survey data.

The findings are useful not only for corporate decision makers in establishing an appropriate dividend policy but also for shareholders in making investment decisions. Because the current study is limited to Sri Lanka, future researchers should study the same phenomenon in other countries using the triangulation approach.

This study provides a hybrid approach to dividend policy research by using both primary and secondary data in a single study. It is the first dividend study in Sri Lanka to use a triangulation approach.

The purpose of this study is to assess the current prevalence of empirical research in the field of social entrepreneurship. Further, we identify secondary datasets and explain their relative strengths and weaknesses for use by social entrepreneurship scholars.

The authors conducted a search of academic articles in the EBSCO and ProQuest databases mentioning social entrepreneurship, social venture(s), social enterprise(s), or social entrepreneur(s) in the title, abstract, or keywords published from 2009 to 2013. Papers were coded and analyzed based upon the nature of their methods.

We find that while qualitative studies are still the norm, quantitative methods are increasing, thanks to the creation of large-scale datasets and the use of analysis techniques new to the field. Three such large-scale datasets – the PSED II, GEM, and nonprofit tax collections – are discussed in depth. We find several strengths and weaknesses for each dataset, yet each provides social entrepreneurship scholars with fruitful opportunities.

Through a deeper understanding of empirical research and sources of social entrepreneurship data, scholars may be more attracted to social entrepreneurship, better equipped to conduct high-quality research and publish in high-quality outlets. Moreover, by moving beyond case studies and small-sample research to engaging larger pools of subjects and producing more generalizable findings, social entrepreneurship scholars will have the ability to impact a much broader scope of practitioners.

Dual diagnosis is a term that describes the co-occurrence of mental health disorders or illness and substance use or abuse disorders. Because this co-occurrence results in multiple diseases, layers of treatment are often needed to successfully create positive change in the individual. The purpose of this study is to explore factors of treatment that could facilitate improvements in functionality and quality of life for those with a dual diagnosis.

A secondary data analysis, using both quantitative and qualitative data, was completed. Secondary analysis is an empirical exercise that applies the same basic research principles as studies using primary data and has steps to be followed, including the evaluative and procedural steps commonly associated with secondary data analysis. Documentation data from the intensive mobile psychosocial assertive community treatment program was gathered for this analysis; this program was used because of the intensive and community-based services provided to patients with a dual diagnosis.

The major findings from this secondary analysis suggested that significant barriers included “denial” (e.g. evasion, suspension or avoidance of internal awareness) of diagnoses, complicated treatment and other barriers related to housing. Ultimately, these findings provided greater insight into potential effective treatment interventions for people living with a dual diagnosis.

This study adds to the growing body of literature showing that patient-centered care allows for more effective treatment and ultimately, improved health outcomes.

The purpose of this paper is to share the research processes and results of secondary analysis using GIS to map usage of a university library to contribute to ongoing efforts to help identify how library spaces are used to explain how university libraries can continue to evolve as teaching, learning, and shared communities of scholars. This paper details the use of ArcGIS to visualize where students are in the library in order to explain how this method can used by libraries to visualize the use of their facilities.

This research utilized secondary analysis of data collected during seating sweeps; through secondary analysis, data were analyzed and visualized in ArcGIS. The seating sweeps were conducted three times a day during a sample week, with researchers noting on maps of the library floor plan where students were sitting. Data were entered into an ArcGIS database file and mapped to display usage directly on the library map to improve stakeholders’ understanding of the ways students are using the library as a place.

Even though this project used consistent instruments and procedural instructions and trained observers, a combination of factors resulted in an incomplete data set, including the length of time between research design and data collection and lack of agreement about the use of map worksheets. It was still possible to make maps that depict heavier and lighter areas of use, present data to library stakeholders, and show what can be accomplished when data are collected on copies of the floor plan.

This research is limited by being a conducted in one university library, but the implications far outweigh the limitations. While bar and pie charts are effective at visualizing data, they do not provide a way to visualize where activities occur; maps provide multi-layered visualization, allowing libraries to visualize the same usage data as bar, pie, or other charts in addition to seeing where that usage occurs. The implications for librarianship include better understanding of how library spaces are used and the ability to use visually appealing maps to demonstrate the library’s use, value, and impact.

Mapping library statistics is an area that has been growing in the last decade, but practical examples of using GIS to map facility usage are few. This paper explains in detail how the mapping process works and how libraries of all types can adapt this method for their own usage assessments to more vividly depict the value and impact of the library facility as a place.

Objective – Measurement of the incidence of health financing contributions across socio-economic groups has proven valuable in informing health care financing reforms. However, there is little evidence as to how to carry out financing incidence analysis (FIA) in lower income settings. We outline some of the challenges faced when carrying out a FIA in Ghana, Tanzania and South Africa and illustrate how innovative techniques were used to overcome data weaknesses in these settings.

Methodology – FIA was carried out for tax, insurance and out-of-pocket (OOP) payments. The primary data sources were Living Standards Measurement Surveys (LSMS) and household surveys conducted in each of the countries; tax authorities and insurance funds also provided information. Consumption expenditure and a composite index of socio-economic status (SES) were used to assess financing equity. Where possible conventional methods of FIA were applied. Numerous challenges were documented and solution strategies devised.

Results – LSMS are likely to underestimate financial contributions to health care by individuals. For tax incidence analysis, reported income tax payments from secondary sources were severely under-reported. Income tax payers and shareholders could not be reliably identified. The use of income or consumption expenditure to estimate income tax contributions was found to be a more reliable method of estimating income tax incidence. Assumptions regarding corporate tax incidence had a huge effect on the progressivity of corporate tax and on overall tax progressivity. LSMS consumption categories did not always coincide with tax categories for goods subject to excise tax (e.g. wine and spirits were combined, despite differing tax rates). Tobacco companies, alcohol distributors and advertising agencies were used to provide more detailed information on consumption patterns for goods subject to excise tax by income category. There was little guidance on how to allocate fuel levies associated with ‘public transport’ use. Hence, calculations of fuel tax on public transport were based on individual expenditure on public transport, the average cost per kilometre and average rates of fuel consumption for each form of transport. For insurance contributions, employees will not report on employer contributions unless specifically requested to and are frequently unsure of their contributions. Therefore, we collected information on total health insurance contributions from individual schemes and regulatory authorities. OOP payments are likely to be under-reported due to long recall periods; linking OOP expenditure and illness incidence questions – omitting preventive care; and focusing on the last service used when people may have used multiple services during an illness episode. To derive more robust estimates of financing incidence, we collected additional primary data on OOP expenditures together with insurance enrolment rates and associated payments. To link primary data to the LSMS, a composite index of SES was used in Ghana and Tanzania and non-durable expenditure was used in South Africa.

Policy implications – We show how data constraints can be overcome for FIA in lower income countries and provide recommendations for future studies.

This paper examines existing research on the topic of personal health records (PHRs). Areas covered include PHR/patient portal, recordkeeping, preservation planning, access and provider needs for future reuse of health information. Patient and physician PHR use and functionality, as well as adoption facilitators and barriers, are also reviewed.

The paper engages in a review of relevant literature from a variety of subject domains, including personal information management, medical informatics, medical literature and archives and records management literature.

The review finds that PHRs are extensions of electronic records. In addition, it finds a lack of literature within archives and records management that may lead to a less preservation-centric examination of the new PHR technologies that are desirable for controlling the lifecycle of these important new records-type.

Although the issues presented by PHRs are issues that can best be solved with the use of techniques from records management, there is no current literature related to PHRs in the records management literature, and that offered in the medical informatics literature treats the stewardship aspects of PHRs as insurmountable. This paper offers an introduction to the aspects of PHRs that could fruitfully be examined in archives and records management.

This article provides an account of how databases can be effectively used in entrepreneurship research. Improved quality and access to large secondary databases offer paths to answer questions of great theoretical value. I present an overview of theoretical, methodological, and practical difficulties in working with database data, together with advice on how such difficulties can be overcome. Conclusions are given, together with suggestions of areas where databases might provide real and important contributions to entrepreneurship research.

The purpose of this paper is to lay out the data competence maturity model (DCMM) and discuss how the application of the model can serve as a foundation for a measured and deliberate use of data in secondary education.

Although the model is new, its implications, and its application are derived from key findings and best practices from the software development, data analytics and secondary education performance literature. These principles can guide educators to better manage student and operational outcomes. This work builds and applies the DCMM model to secondary education.

The conceptual model reveals significant opportunities to improve data-driven decision making in schools and local education agencies (LEAs). Moving past the first and second stages of the data competency maturity model should allow educators to better incorporate data into the regular decision-making process.

Moving up the DCMM to better integrate data into their decision-making process has the potential to produce profound improvements for schools and LEAs. Data science is about making better decisions. Understanding the path laid out in the DCMM to helping an organization move to a more mature data-driven decision-making process will help improve both student and operational outcomes.

This paper brings a new concept, the DCMM, to the educational literature and discusses how these principles can be applied to improve decision making by integrating them into their decision-making process and trying to help the organization mature within this framework.