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The purpose of this paper is to provide insightful evidence of phenomena in organization and management theory. Textual data sets consist of two different elements, namely qualitative and quantitative aspects. Researchers often combine methods to harness both aspects. However, they frequently do this in a comparative, convergent, or sequential way.

The paper illustrates and discusses a hybrid textual data analysis approach employing the qualitative software application GABEK-WinRelan in a case study of an Austrian retail bank.

The paper argues that a hybrid analysis method, fully intertwining qualitative and quantitative analysis simultaneously on the same textual data set, can deliver new insight into more facets of a data set.

A hybrid approach is not a universally applicable solution to approaching research and management problems. Rather, this paper aims at triggering and intensifying scientific discussion about stronger integration of qualitative and quantitative data and analysis methods in management research.

This study aims to explore whether Chat Generative Pre-training Transformer (ChatGPT) can produce quantitative data sets for researchers who could behave unethically through data fabrication.

A two-stage case study related to the field of tourism was conducted, and ChatGPT (v.3.5.) was asked to respond to the first questionnaire on behalf of 400 participants and the second on behalf of 800 participants. The artificial intelligence (AI)-generated data sets’ quality was statistically tested via descriptive statistics, correlation analysis, exploratory factor analysis, confirmatory factor analysis and Harman's single-factor test.

The results revealed that ChatGPT could respond to the questionnaires as the number of participants at the desired sample size level and could present the generated data sets in a table format ready for analysis. It was also observed that ChatGPT's responses were systematical, and it created a statistically ideal data set. However, it was noted that the data produced high correlations among the observed variables, the measurement model did not achieve sufficient goodness of fit and the issue of common method bias emerged. The conclusion reached is that ChatGPT does not or cannot yet generate data of suitable quality for advanced-level statistical analyses.

This study shows that ChatGPT can provide quantitative data to researchers attempting to fabricate data sets unethically. Therefore, it offers a new and significant argument to the ongoing debates about the unethical use of ChatGPT. Besides, a quantitative data set generated by AI was statistically examined for the first time in this study. The results proved that the data produced by ChatGPT is problematic in certain aspects, shedding light on several points that journal editors should consider during the editorial processes.

本研究旨在探讨ChatGPT是否能够为那些可能通过数据伪造行为不道德的研究人员生成定量数据集。

本研究进行了与旅游领域相关的两阶段案例研究, 并要求ChatGPT（v.3.5.）代表400名参与者回答第一个问卷, 以及代表800名参与者回答第二个问卷。通过描述统计、相关分析、探索性因子分析、验证性因子分析和哈曼的单因素测试对人工智能生成的数据集的质量进行了统计测试。

结果显示, ChatGPT能够按照所需的样本大小水平回答问卷, 并以表格格式呈现生成的数据集, 以便进行分析。还观察到ChatGPT的回答是系统性的, 并且它创建了一个在统计上理想的数据集。然而, 本研究注意到所产生的数据在观察变量之间存在较高的相关性, 测量模型未能达到足够的拟合度, 并出现了共同方法偏差的问题。本研究得出的结论是, ChatGPT目前不能生成适用于高级统计分析的数据, 或者说不适合这样做。

本研究表明, ChatGPT可以为试图不道德地伪造数据集的研究人员提供定量数据。因此, 它为关于ChatGPT不道德使用的持续争论提供了一个新而重要的论点。此外, 在本研究中首次对由人工智能生成的定量数据集进行了统计检验。结果表明, ChatGPT生成的数据在某些方面存在问题, 为期刊编辑在编辑过程中考虑的几个要点提供了启示。

The purpose of this paper is to discuss the use of mixed methods research in a major three year project and focuses on the contribution of quantitative and qualitative approaches to study school improvement. It discusses the procedures and multiple data sources used in studying improvement using the example of a recent study of the role of leadership in promoting improvement in primary and secondary schools’ academic results in England. Although the definition of improvement used was based on robust analyses of data on students’ academic outcomes, the mixed methods design enabled a broader perspective to be achieved.

The study illustrates how the multilevel analysis of students’ national assessment and examination results based on national data sets for primary and secondary schools in England were used to investigate the concept of academic effectiveness based on value-added methodology. Using three successive years of national results a purposive sample of schools were identified that could be classified as both effective and improving over the period 2003-2005. In addition, surveys and interviews were used to gather evidence of the role of stakeholder perceptions in investigating school improvement strategies and processes.

National student attainment data sets were used for the identification of improving and effective schools and revealed the importance of considering their different starting points in their classification of three distinctive improvement groups. The combination of quantitative survey data from headteachers and key staff with qualitative case study data enabled a range of analysis strategies and the development of statistical models and deeper understanding of the role of leadership.

The limitations of a focus on only academic outcomes and “value-added” measures of student progress are discussed. The challenges and opportunities faced in analysis and integration of the different sources of evidence are briefly explored.

The study contributes to the knowledge base on the identification of school improvement and use of performance data. The findings on strategies and processes that support improvement are of relevance to policy makers and practitioners, especially school leaders.

The mixed methods design adopted in the study enabled the research to combine rigorous quantitative and in-depth qualitative data in new ways to extend and make new claims to knowledge about the role of school leadership in promoting school improvement based on the study of effective and improved schools’ experiences.

This paper aims to explore the possibilities in using such national, statutory data sets for evaluating change and the challenges of understanding service patterns and outcomes in complex cases when only a limited view can be gained using existing data. The discussion also explores how methodologies can adapt to an evaluation in these circumstances.

This paper examines the use of data routinely collected by local authorities (LAs) as part of the evaluation of innovation. Issues entailed are discussed and illustrated through two case studies of evaluations conducted by the research team within the context of children’s social care in England.

The quantitative analysis of LA data can play an important role in evaluating innovation but researchers will need to address challenges related to: selection of a suitable methodology; identifying appropriate comparator data; accessing data and assessing its quality; and sustaining and increasing the value of analytic work beyond the end of the research. Examples are provided of how the two case studies experienced and addressed these challenges.

• Quasi-experimental methods can be beneficial tools for understanding the impact of innovation in children’s services, but researchers should also consider the complexity of children’s social care and the use of mixed and appropriate methods. • Those funding innovative practice should consider the additional burden on those working with data and the related data infrastructure if wishing to document and analyse innovation in a robust way. • Data, which may be assumed to be uniform may in fact not be when considered at a multi-area or national level, and further study of the data recording practice of social care professionals is required.

The paper discusses some common issues experienced in quasi-experimental approaches to the quantitative evaluation of children’s services, which have, until recently, been rarely used in the sector. There are important considerations, which are of relevance to researchers, service leads in children’s social care, data and performance leads and funders of innovation.

With increased focus on data literacy and data science education in K-12, little is known about what makes a data set preferable for use by classroom teachers. Given that educational designers often privilege authenticity, the purpose of this study is to examine how teachers use features of data sets to determine their suitability for authentic data science learning experiences with their students.

Interviews with 12 practicing high school mathematics and statistics teachers were conducted and video-recorded. Teachers were given two different data sets about the same context and asked to explain which one would be better suited for an authentic data science experience. Following knowledge analysis methods, the teachers’ responses were coded and iteratively reviewed to find themes that appeared across multiple teachers related to their aesthetic judgments.

Three aspects of authenticity for data sets for this task were identified. These include thinking of authentic data sets as being “messy,” as requiring more work for the student or analyst to pore through than other data sets and as involving computation.

Analysis of teachers’ aesthetics of data sets is a new direction for work on data literacy and data science education. The findings invite the field to think critically about how to help teachers develop new aesthetics and to provide data sets in curriculum materials that are suited for classroom use.

Neuroscience data are spread across a variety of sources, typically provisioned through ad-hoc and non-standard approaches and formats and often have no connection to the related data sources. These make it difficult for researchers to understand, integrate and reuse brain-related data. The aim of this study is to show that a graph-based approach offers an effective mean for representing, analysing and accessing brain-related data, which is highly interconnected, evolving over time and often needed in combination.

The authors present an approach for organising brain-related data in a graph model. The approach is exemplified in the case of a unique data set of quantitative neuroanatomical data about the murine basal ganglia––a group of nuclei in the brain essential for processing information related to movement. Specifically, the murine basal ganglia data set is modelled as a graph, integrated with relevant data from third-party repositories, published through a Web-based user interface and API, analysed from exploratory and confirmatory perspectives using popular graph algorithms to extract new insights.

The evaluation of the graph model and the results of the graph data analysis and usability study of the user interface suggest that graph-based data management in the neuroscience domain is a promising approach, since it enables integration of various disparate data sources and improves understanding and usability of data.

The study provides a practical and generic approach for representing, integrating, analysing and provisioning brain-related data and a set of software tools to support the proposed approach.

To understand the unique context forming organizational learning, the current study aims to investigate the antecedents of training and development (TD) practices as an indicator of effective organizational learning from the Vietnam hospitality sector, which has not been studied rigorously.

This study adopts a mixed method of quantitative and qualitative data analysis, including a path analysis partial least squares structural equation modeling (PLS-SEM) based on a sample size of 354 responses and a semi-structured interview of 32 participants to test various paths predicting training effectiveness while exploring contextual differences in the Vietnam hospitality sector.

The results show that among the variables investigated, extrinsic motivation, team support and job quality were found to be significant to TD, while intrinsic motivation was found to have no significant predictive power. To explore the reasons behind these findings, the interviews indicate that the motivations of employees in the hospitality sector, most of whom are young and have a limited length of service, are highly rooted in the organization’s cultural context.

This study contributes to understanding the complex context of organizational learning through an investigation of an emerging economy from Southeast Asia by adding new insights into the training and motivational theories. It has practical implications for practitioners in the hospitality sector to develop more effective learning organizations during the uncertain period of this unprecedented pandemic.

The purpose of this paper is to identify the factors influencing the scale and nature of intercountry adoption (ICA) between the People’s Republic of China and the USA, and to describe the significance and contribution of each to ICA processes.

A documentary data analysis approach based upon the quantitative grounded theory: first, interpreting available data, and second, conducting a thematic analysis of the literature to generate a theory of key factors.

The results showed that changes in policies, ethical narratives and ideological shifts (principally the rise of nationalism) were highly influential in determining the scale and type of ICAs in successive years.

This paper concluded that China: US ICA is likely to continue only in small numbers with older and special needs children. However, China: US adoptions provide some examples of “best practice”. Understanding the interplay of factors explored theoretically in this study may guide future ICA arrangements between other country pairs.

Although a range of data has been collected on China: US ICA over a number of years, no systematic attempt has been made to link changes in those data to changes in the legal, social or cultural climate in which such adoptions take place. As well as providing new insights into the dynamics of ICA, the paper develops an original method which could be applied to parallel arrangements between other countries.

Longitudinal investigations are often suggested but rarely used in operations and supply chain management (OSCM), mainly due to the difficulty of obtaining data. There is a silver lining in the form of existing large-scale and planned repeated cross-sectional (RCS) data sets, an approach commonly used in sociology and political sciences. This study aims to review all relevant RCS surveys with a focus on OSCM, as well as data and methods to motivate longitudinal research and to study trends at the plant, industry and geographic levels.

A comparison of RCS, panel and hybrid surveys is presented. Existing RCS data sets in the OSCM discipline and their features are discussed. In total, 30 years of Global Manufacturing Research Group data are used to explore the applicability of analytical methods at the plant and aggregate level and in the form of multilevel modeling.

RCS analysis is a viable alternative to overcome the confines associated with panel data. The structure of the existing data sets restricts quantitative analysis due to survey and sampling issues. Opportunities surrounding RCS analysis are illustrated, and survey design recommendations are provided.

The longitudinal aspect of RCS surveys can answer new and untested research questions through repeated random sampling in focused topic areas. Planned RCS surveys can benefit from the provided recommendations.

RCS research designs are generally overlooked in OSCM. This study provides an analysis of RCS data sets and future survey recommendations.

This chapter focuses on common business challenges where intelligent choices and behaviors may lead to new and different outcomes. The business stories represent a wide range of economic sectors, types of organizations, and challenges. Each story highlights the role the framework plays in deriving and realizing an intelligent solution.