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Content analysis is a family of systematic, rule-guided techniques used to analyze the informational contents of textual data (Mayring, 2000). It is used frequently in nursing research, and is rapidly becoming more prominent in the medical and bioethics literature. There are several types of content analysis including quantitative and qualitative methods all sharing the central feature of systematically categorizing textual data in order to make sense of it (Miles & Huberman, 1994). They differ, however, in the ways they generate categories and apply them to the data, and how they analyze the resulting data. In this chapter, we describe a type of qualitative content analysis in which categories are largely derived from the data, applied to the data through close reading, and analyzed solely qualitatively. The generation and application of categories that we describe can also be used in studies that include quantitative analysis.

This research explores perceptions of knowledge management processes held by managers and employees in a service industry. To date, empirical research on knowledge management in the service industry is sparse. This research seeks to examine absorptive capacity and its four capabilities of acquisition, assimilation, transformation and exploitation and their impact on effective knowledge management. All of these capabilities are strategies that enable external knowledge to be recognized, imported and integrated into, and further developed within the organization effectively. The research tests the relationships between absorptive capacity and effective knowledge management through analysis of quantitative data (n = 549) drawn from managers and employees in 35 residential aged care organizations in Western Australia. Responses were analysed using Partial Least Square-based Structural Equation Modelling. Additional analysis was conducted to assess if the job role (of manager or employee) and three industry context variables of profit motive, size of business and length of time the organization has been in business, impacted on the hypothesized relationships.

Structural model analysis examines the relationships between variables as hypothesized in the research framework. Analysis found that absorptive capacity and the four capabilities correlated significantly with effective knowledge management, with absorptive capacity explaining 56% of the total variability for effective knowledge management. Findings from this research also show that absorptive capacity and the four capabilities provide a useful framework for examining knowledge management in the service industry. Additionally, there were no significant differences in the perceptions held between managers and employees, nor between respondents in for-profit and not-for-profit organizations. Furthermore, the size of the organization and length of time the organization has been in business did not impact on absorptive capacity, the four capabilities and effective knowledge management.

The research considers implications for business in light of these findings. The role of managers in providing leadership across the knowledge management process was confirmed, as well as the importance of guiding routines and knowledge sharing throughout the organization. Further, the results indicate that within the participating organizations there are discernible differences in the way that some organizations manage their knowledge, compared to others. To achieve effective knowledge management, managers need to provide a supportive workplace culture, facilitate strong employee relationships, encourage employees to seek out new knowledge, continually engage in two-way communication with employees and provide up-to-date policies and procedures that guide employees in doing their work. The implementation of knowledge management strategies has also been shown in this research to enhance the delivery and quality of residential aged care.

The overall goal of science is to build a valid and reliable body of knowledge about the functioning of the world and how applying that knowledge can change it. As personnel and human resources management researchers, we aim to contribute to the respective bodies of knowledge to provide both employers and employees with a workable foundation to help with those problems they are confronted with. However, what research on research has consistently demonstrated is that the scientific endeavor possesses existential issues including a substantial lack of (a) solid theory, (b) replicability, (c) reproducibility, (d) proper and generalizable samples, (e) sufficient quality control (i.e., peer review), (f) robust and trustworthy statistical results, (g) availability of research, and (h) sufficient practical implications. In this chapter, we first sing a song of sorrow regarding the current state of the social sciences in general and personnel and human resources management specifically. Then, we investigate potential grievances that might have led to it (i.e., questionable research practices, misplaced incentives), only to end with a verse of hope by outlining an avenue for betterment (i.e., open science and policy changes at multiple levels).

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.

The study aims to develop a model that supports the application of data science techniques for real estate professionals in the fourth industrial revolution (4IR) era. The present 4IR era gave birth to big data sets and is beyond real estate professionals' analysis techniques. This has led to a situation where most real estate professionals rely on their intuition while neglecting a rigorous analysis for real estate investment appraisals. The heavy reliance on their intuition has been responsible for the under-performance of real estate investment, especially in Africa.

This study utilised a survey questionnaire to randomly source data from real estate professionals. The questionnaire was analysed using a combination of Statistical package for social science (SPSS) V24 and Analysis of a Moment Structures (AMOS) graphics V27 software. Exploratory factor analysis was employed to break down the variables (drivers) into meaningful dimensions helpful in developing the conceptual framework. The framework was validated using covariance-based structural equation modelling. The model was validated using fit indices like discriminant validity, standardised root mean square (SRMR), comparative fit index (CFI), Normed Fit Index (NFI), etc.

The model revealed that an inclusive educational system, decentralised real estate market and data management system are the major drivers for applying data science techniques to real estate professionals. Also, real estate professionals' application of the drivers will guarantee an effective data analysis of real estate investments.

Numerous studies have clamoured for adopting data science techniques for real estate professionals. There is a lack of studies on the drivers that will guarantee the successful adoption of data science techniques. A modern form of data analysis for real estate professionals was also proposed in the study.

Within the last 15 years, law enforcement agencies have increased their collection of data on vehicle stops. A variety of resource guides, research reports, and peer‐reviewed articles have outlined the methods used to collect these data and conduct analyses. This literature is spread across numerous publications and can be cumbersome to summarize for practical use by practitioners and academics. This article seeks to fill this gap by detailing the current best practices in vehicle stop data collection and analysis in state police agencies.

The article summarizes the data collection techniques used to assist in identifying racial/ethnic disparities in vehicle stops. Specifically, questions concerning why, when, how, and what data should be collected are addressed. The most common data analysis techniques for vehicle stops are offered, including an evaluation of common benchmarking techniques and their ability to measure at‐risk drivers. Vehicle stop outcome analyses are also discussed, including multivariate analyses and the outcome test. Within this summary, strengths and weaknesses of these techniques are explored.

In summarizing these approaches, a body of best practices in vehicle stop data collection and analysis is developed.

Racial profiling continues to be a contentious issue for law enforcement and the community. A considerable body of research has developed to assess the prevalence of racial profiling. This article offers social scientists and practitioners a comprehensive, succinct, peer‐reviewed summary of the best practices in vehicle stop data collection and analysis.

There is a lack of a framework that explicates how to determine the benefits that consumers desire from a product. The purpose of this article is to formulate a scientific procedure for discerning the benefits that consumers seek from a product. The authors term this procedure as visual thematic analysis (VTA). VTA procedure is illustrated through discerning the benefits of mainstream (non‐financial) English newspapers.

The focus group method was used to collect data. These data were analyzed using visual thematic analysis which involves using multiple investigators and multi‐dimensional scaling techniques in stages.

A total of 26 newspaper attributes combined to form eight distinct newspaper benefits namely ease of comprehension, journalistic values, critical insights, general news, entertainment, well‐being, classifieds and offers.

Obtained results may be used further: to segment the newspaper market based upon benefits sought, to position newspapers within the desired segment(s) and to fashion product mix in a way that appeals to the targeted segment(s).

This paper proposes a new method called “visual thematic analysis” for data reduction. One such application of VTA is “discerning product benefits” which is discussed in detail. Other applications of this technique that are mentioned in the paper are in the areas of data reduction when researcher confronts small sample size, data reduction of categorical variables and scale development.