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Data visualizations in some form or another have served as decision-support tools for many centuries. In conjunction with advancements in information technology, data visualizations have become more accessible and more efficient to generate. In fact, virtually all enterprise resource planning and human resource (HR) information system vendors offer off-the-shelf data visualizations as part of decision-support dashboards as well as stand-alone images and displays for reporting. Plus, advances in programing languages and software such as Tableau, Microsoft Power BI, R, and Python have expanded the possibilities of fully customized graphics. Despite the proliferation of data visualization, relatively little is known about how to design data visualizations for displaying different types of HR data to different user groups, for different purposes, and with the overarching goal of improving the ways in which users comprehend and interpret data visualizations for decision-making purposes. To understand the state of science and practice as they relate to HR data visualizations and data visualizations in general, we review the literature on data visualizations across disciplines and offer an organizing framework that emphasizes the roles data visualization characteristics (e.g., display type, features), user characteristics (e.g., experience, individual differences), tasks, and objectives (e.g., compare values) play in user comprehension, interpretation, and decision-making. Finally, we close by proposing future directions for science and practice.

The purpose of this paper is to explore the role of storytelling in data interpretation, decision-making and individual-level adoption of business analytics (BA).

Existing theory is extended by introducing the concept of BA data-driven storytelling and by synthesizing insights from BA, storytelling, behavioral research, linguistics, psychology and neuroscience. Using theory-building methodology, a model with propositions is introduced to demonstrate the relationship between storytelling, data interpretation quality, decision-making quality, intention to use BA and actual BA use.

BA data-driven storytelling is a narrative sensemaking heuristic positively influencing human behavior towards BA use. Organizations deliberately disseminating BA data-driven stories can improve the quality of individual data interpretation and decision-making, resulting in increased individual utilization of BA on a daily basis.

To acquire a deeper understanding of BA data-driven storytelling in behavioral operational research (BOR), future studies should test the theoretical model of this study and focus on exploring the complexity and diversity in individual attitudes toward BA.

This study provides practical guidance for business practitioners who struggle with interpreting vast amounts of complex data, making data-driven decisions and incorporating BA into daily operations.

This cross-disciplinary study develops existing BOR, storytelling and BA literature by showing how a novel BA data-driven storytelling approach can facilitate BA adoption in organizations.

Drawing on the resource-based theory and dynamic capability view, this paper aims to examine the mechanisms by which business analytics (BA) capabilities (i.e. the effective use of data aggregation, analytics and data interpretation tools) in healthcare units indirectly influence decision-making effectiveness through the mediating role of knowledge absorptive capacity.

Using a survey method, this study collected data from the hospitals in Taiwan. Of the 155 responses received, three were incomplete, giving a 35.84 per cent response rate with 152 valid data points. Structural equation modeling was used to test the hypotheses.

This study conceptualizes, operationalizes and measures the BA capability as a multi-dimensional construct that is formed by capturing the functionalities of BA systems in health care, leading to the conclusion that healthcare units are likely to obtain valuable knowledge through using the data analysis and interpretation tools effectively. The effective use of data analysis and interpretation tools in healthcare units indirectly influence decision-making effectiveness, an impact that is mediated by absorptive capacity.

This study adds values to the literature by conceptualizing BA capabilities in healthcare and demonstrating how knowledge absorption matters when implementing BA to the decision-making process. The mediating role of absorptive capacity not only provides a mechanism by which BA can contribute to decision-making practices but also offers a new solution to the puzzle of the IT productivity paradox in healthcare settings.

This paper discusses the ways that software programs can support qualitative market research practitioners in data analysis and interpretation. First it looks at what these programs entail and shows how certain misconceptions have arisen around their use. Then it describes how one particular program, NUD*IST, can be used in the analysis and interpretation process and relates this to its use by market research practitioners.

To discuss and analyse three themes in qualitative research in marketing which are objects of both frustration and confusion: analysis and interpretation; theory generation; and a quest for scientific pluralism and individual researcher lifestyles.

Underpinning the discussion is that complexity, ambiguity, fuzziness, chaos, change, uncertainty and unpredictability are characteristics of a market economy; that qualitative and subjective interpretation is necessary to add the spark of life to marketing data; and that general marketing theory needs more attention from researchers.

The proper use of methodology and the generation of better marketing theory will make it easier for practitioners to reach the right decisions.

Quantitative and qualitative research processes are not by nature antagonistic, although their advocates may be; quantitative methodology carries qualitative “bugs”, necessary for its sustenance.

The article ends with a recommendation that every researcher in marketing should design his or her individual research approach, one that suits the personality of the researcher. As an example, the author presents his own current methodology‐in‐use, interactive research.

This paper explores the challenges of conducting qualitative research from ELT (English Language Teaching) Ph.D. candidates' perspectives.

The participants of the study consisted of 30 Iranian Ph.D. students majoring in ELT. The semi-structured interview was employed to investigate the heart of experiences, issues and concerns of participants with regard to conducting qualitative research (QLR) challenges. To analyze the collected data, the recorded interviews were transcribed, and then the grounded theory approach was employed (Charmaz, 2006).

The results revealed that the major challenges of the participants consist of the credibility of QLR in ELT contexts, hermeneutic and fuzzy nature of QLR, qualitative data analysis and interpretation, publishing qualitative findings and the system of measuring professors' productivity.

The findings may help professors, mainly EFL ones, in research mentoring and developing research syllabi for graduate students. In addition, it may motivate Ph.D. candidates to employ QLR methods in their research studies. The pedagogical and theoretical implications of the study are discussed at the end of the paper.

The objective of the paper is to amalgamate theories of text retrieval from various research traditions into a cognitive theory for information retrieval interaction. Set in a cognitive framework, the paper outlines the concept of polyrepresentation applied to both the user's cognitive space and the information space of IR systems. The concept seeks to represent the current user's information need, problem state, and domain work task or interest in a structure of causality. Further, it implies that we should apply different methods of representation and a variety of IR techniques of different cognitive and functional origin simultaneously to each semantic full‐text entity in the information space. The cognitive differences imply that by applying cognitive overlaps of information objects, originating from different interpretations of such objects through time and by type, the degree of uncertainty inherent in IR is decreased. Polyrepresentation and the use of cognitive overlaps are associated with, but not identical to, data fusion in IR. By explicitly incorporating all the cognitive structures participating in the interactive communication processes during IR, the cognitive theory provides a comprehensive view of these processes. It encompasses the ad hoc theories of text retrieval and IR techniques hitherto developed in mainstream retrieval research. It has elements in common with van Rijsbergen and Lalmas' logical uncertainty theory and may be regarded as compatible with that conception of IR. Epistemologically speaking, the theory views IR interaction as processes of cognition, potentially occurring in all the information processing components of IR, that may be applied, in particular, to the user in a situational context. The theory draws upon basic empirical results from information seeking investigations in the operational online environment, and from mainstream IR research on partial matching techniques and relevance feedback. By viewing users, source systems, intermediary mechanisms and information in a global context, the cognitive perspective attempts a comprehensive understanding of essential IR phenomena and concepts, such as the nature of information needs, cognitive inconsistency and retrieval overlaps, logical uncertainty, the concept of ‘document’, relevance measures and experimental settings. An inescapable consequence of this approach is to rely more on sociological and psychological investigative methods when evaluating systems and to view relevance in IR as situational, relative, partial, differentiated and non‐linear. The lack of consistency among authors, indexers, evaluators or users is of an identical cognitive nature. It is unavoidable, and indeed favourable to IR. In particular, for full‐text retrieval, alternative semantic entities, including Salton et al.'s ‘passage retrieval’, are proposed to replace the traditional document record as the basic retrieval entity. These empirically observed phenomena of inconsistency and of semantic entities and values associated with data interpretation support strongly a cognitive approach to IR and the logical use of polyrepresentation, cognitive overlaps, and both data fusion and data diffusion.

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.

Purpose – To present the Assessment to Instructional Planning (ATIP) framework that uses assessment to guide instructional planning.

Design/methodology/approach – The ATIP framework is comprised of three interconnected processes: data collection, data analysis and interpretation, and instructional planning.

Findings – In the ATIP framework, data collection includes reviewing background information and developing and implementing an assessment plan. The data analysis and interpretation process begins with scoring assessments and progresses to contextualizing results and making decisions. Instructional planning moves from setting goals to selecting instructional methods and materials, implementing instructional checkpoints, and monitoring and adjusting instruction.

Research limitations/implications – The ATIP framework provides a step-by-step process that educators can follow to use assessment to plan instruction. ATIP requires that educators already have knowledge of literacy assessment and instruction to apply the Framework appropriately.

Practical implications – The ATIP framework can be applied for students in grades K-8 in clinical settings, school-based intervention programs, and elementary and middle school classrooms.

Originality/value – This chapter provides three profiles to illustrate the ATIP framework in clinical, small-group intervention, and classroom settings with different levels of readers with varying strengths, needs, and backgrounds.